Job Board

Title: Visual recognition with minimal supervision in deep learning context

The goal of this PhD is to study object detection/segmentation in images or video with minimal supervision. This task will be placed into a setting where only image-level annotation is provided. To begin, additional supervision such as clicks, strokes, or bounding boxes may also be assumed. Towards the end of the PhD, the student is expected to work with datasets of mixed levels of supervision, including a harder, semi-supervised setting where there are only a few image-level labels as well as a large amount of unlabeled images. Few-shot learning is another challenging direction to explore.

Several ideas can be investigated in the context of deep learning. For instance, generative adversarial learning can be employed to either augment the dataset or bridge the predicted detection with their ground truth. Recurrent neural networks can be applied to video segmentation in particular to localize and segment semantic parts across nearby frames. On unstructured image datasets, ideas like random-walk label propagation can be extended across pairs or groups of images. Deep metric learning and cross-category transfer learning can be studied in a few-shot scenario.

The candidate should ideally have a master degree in computer science, applied mathematics or electrical engineering; solid mathematical background and programming skills; fluency in English language; prior experience in computer vision, machine learning and deep learning.

This is a UK/EU studentship for three years. The target starting date is Oct. 2020. The PhD will be supervised by Dr Miaojing Shi and Dr Michael Spratling. Work will be carried out within the Department of Informatics, King’s College London. More details can be found here:https://www.kcl.ac.uk/informatics/postgraduate/research-degrees

Application Instructions: Candidates are requested to send an initial expression of interest to Miaojing Shi (miaojing.shi@kcl.ac.uk) preferably with updated CV and motivation letter.

The DIL at Adobe Research (https://research.adobe.com/) in Maryland is looking for interns to work on a range of problems in computer vision (CV) and natural language processing (NLP). The interns will be supervised by researchers in groups who have excellent publication records with dozens of papers at top-tier machine learning and AI conferences and journals in recent years. Our research topics include:

- Vision-Langauge tasks (Image captioning, VQA, Reasoning, Scene Graph Generation, Cross-Modal Retrieval, etc.)
- Computer Vision / Natural Language Processing tasks

Preferred Qualifications: Candidates with publications in NLP/CV related conferences (ACL, EMNLP, NAACL, ICLR, NIPS, CVPR, ICCV) are strongly preferred.

NIH Post-doctoral Fellowship in Medical Image Processing – Machine Learning & Computer-Aided Diagnosis

A post-doctoral fellowship is available for 2D/2.5D/3D/4D radiology image processing in Bethesda, Maryland, USA. Specific interest areas are deep learning, image segmentation, modeling, visualization, pattern recognition, computer-aided diagnosis, multi-organ models, atlases and registration. In particular, advanced skills in image processing (computer vision, mathematical modeling, optimization, machine learning) are sought. The researcher will work closely with staff scientists, imaging specialists and clinicians and have access to state-of-the-art whole body MRI, MRI-PET, low-dose CT scanners, advanced graphics workstations and parallel processing/GPU clusters.

Basic Qualifications: Ph.D. in Computer Science, Electrical Engineering, or related discipline with experience in Computer Vision, Machine Learning, or Image
Understanding domain, along with successful demonstration of key responsibilities.

Desirable Qualifications: Strong theoretical and practical background in computer vision, image and video analysis, such as object detection and recognition, statistical pattern recognition, machine learning, sparse methods and applied optimization. Prior knowledge about medical imaging is a plus but not a must. Enthusiasm in solving real world clinical imaging problems using large datasets, and hands-on coding skills and ability in C++ and Matlab.

This appointment is for one year and is renewable thereafter on a periodic basis (up to five years). Applications should include a CV, brief statement of research interests and three letters of reference. DHHS and NIH are Equal Opportunity Employers.

Application Instructions:

Email application materials to Dr. Ronald Summers at rms@nih.gov.
Ronald Summers, M.D., Ph.D.
Chief, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory
E-mail: rms@nih.gov and yingying.zhu@nih.gov

http://www.cc.nih.gov/about/SeniorStaff/ronald_summers.html

The Role

As a member of the Autopilot AI team you will research, design, implement, optimize and deploy deep learning models that advance the state of the art in perception and control for autonomous driving. A typical day to day includes reading deep learning papers, implementing described models and algorithms, adapting them to our setting and driving up internal metrics. A strong candidate will ideally possess at least one strong expertise in the following areas, and at least a familiarity in others.

Responsibilities

Train machine learning and deep learning models on a computing cluster to perform visual recognition tasks, such as segmentation and detection

Develop state-of-the-art algorithms in one or all of the following areas: deep learning (convolutional neural networks), object detection/classification, tracking, multi-task learning, large-scale distributed training, multi-sensor fusion, etc.

Optimize deep neural networks and the associated preprocessing/postprocessing code to run efficiently on an embedded device

Requirements

The team operates in a production setting. An ideal candidate has strong software engineering practices and is very comfortable with Python programming, debugging/profiling, and version control.

We train neural networks on a cluster in large-scale distributed settings. An ideal candidate is very comfortable in cluster environments and understands the related computer systems concepts (CPU/GPU interactions/transfers, latency/throughput bottlenecks during training of neural networks, CUDA, pipelining/multiprocessing, etc).

We are at the cutting edge of deep learning applications. The ideal candidate has a strong understanding of the under the hood fundamentals of deep learning (layer details, backpropagation, etc). Additional requirements include the ability to read and implement related academic literature and experience in applying state of the art deep learning models to computer vision (e.g. segmentation, detection) or a closely related area (speech, NLP).

Experience with PyTorch, or at least another major deep learning framework such as TensorFlow, MXNet.

Some experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment.

The NIH Imaging Biomarkers and Computer-Aided Diagnosis Laboratory is world-renowned, with recent successes including public release of the NIH Chest X-Ray Dataset, the DeepLesion Dataset and multiple MICCAI and CVPR papers. We are seeking a Staff Scientist to support our research and clinical efforts in machine learning/image understanding (large scale robust statistical learning, deep learning, and pattern mining from medical images and patient reports, semantic organ/pathology segmentation, registration, volumetric visualization and algorithm development), image processing, informatics (natural language processing) and computer-aided diagnosis (feature extraction, classification, database development, ROC analysis, validation). In particular, advanced skills in 2D/3D/4D image parsing, segmentation, and building high performance computer-aided detection systems are sought. The incumbent will work closely with a team of computer scientists, engineers and radiology technologists and mentor post-doctoral and other trainees. In addition to performing collaborative research, the incumbent will interact closely with the PACS/RIS section, the Clinical Image Processing Service and the clinical staff in Radiology and other NIH departments.

Applicants with a proven track record as evidenced by peer-reviewed publications on medical imaging applications of machine learning/image understanding and having advanced mathematical and computer skills are encouraged to apply. Demonstrated expertise in deep learning frameworks, in addition to C/C++, Matlab and/or Python, are required. The candidate should have a Ph.D. in Computer Science, Electrical or Biomedical Engineering, Mathematics, Biophysics or Physics. The ideal candidate will have outstanding research experiences or achievements in leading industrial/academic research labs or have completed a productive post-doctoral fellowship at a top-tier program. Salary commensurate with experience. Applications should include a CV, brief statement of research interests, and three letters of reference.

This is a Title 42 federal appointment in the excepted service. This appointment offers federal benefits. A full benefits package includes retirement, health, life and long term care insurance, Thrift Savings Plan participation, etc.

HHS and NIH are Equal Opportunity Employers. Selection for this position will be based solely on merit, without discrimination for non-merit reasons such as race, color, religion, sex, national origin, politics, marital status, sexual orientation, physical or mental handicap, age or membership or non-membership in an employee organization. All applicants will be subject to a background investigation.

Address applications to:
Ronald Summers, M.D., Ph.D.
Chief, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory
Radiology and Imaging Sciences
National Institutes of Health Clinical Center
E-mail: rms@nih.gov, Web site: http://www.cc.nih.gov/about/SeniorStaff/ronald_summers.html

OPTIMA Lab (http://optima.meduniwien.ac.at) is seeking exceptionally motivated post-doctoral scholar to strengthen our interdisciplinary medical image computing team. You will be leading exciting projects in machine learning for health.

The focus of the research project is on automated characterization of retinal pathology from 3D optical coherence tomography (OCT) images of human eye, and on learning and predicting patient-specific disease progression from very large-scale multimodal imaging and electronic health records data.

The successful candidate will be immersed into an interdisciplinary environment working closely with a team of PhD students and Postdocs, research engineers and medical doctors. Advancements will have a real world impact on clinical management of patients suffering from retinal diseases, a leading cause of blindness today

Your profile:
- PhD degree (or soon to be completed) in a relevant discipline (computer science, applied mathematics or related field)
- Strong background in machine and deep learning, and computer vision or medical image analysis
- Relevant publication record in the field
- Excellent programming skills (Python, Julia, C/C++)
- Excellent analytical, interpersonal, as well as written and oral communication skills in English

Application (by email to the contact below):
- Your cover letter with a summary of your previous and present scientific activities and your motivations
- CV
- The 2 most significant publications
- Contact information of three references

Environment:
Our research focus lies on the quantitative analysis of state-of-the-art ophthalmic imaging data, in particular OCT, and the development of prognostic disease models for improved patient management in leading eye diseases. The lab has access to extremely large sets of ophthalmic images and is well-equipped with a dedicated high-performance computing cluster containing the latest generation GPUs. The group keeps close collaboration with several high-performance academic research institutions, as well as partnership with imaging device and pharmaceutical companies.

Medical University of Vienna is an international center of excellence and one of the largest research institutions in Europe located at the heart of historic Vienna. Vienna has recently been named the world's most liveable city for the tenth time!

Apply by 25nd March 2020

The successful candidate will be part of the DeepCamera Research Group and will conduct applied research by developing the next generation of computer vision applications, imaging and video pipeline that will be integrated into the new concept of DeepCamera.

The position holder will also have the opportunity to participate in the preparation of research proposals for funding, project reports and deliverables, and travel abroad for dissemination activities. Furthermore, the position holder will have the opportunity to publish/present their research results in prestigious international conferences and journals and contribute to the research centre’s key performance indicators. The successful candidate will work under the supervision of the Team Leader of the DeepCamera Research Group Dr. Alessandro Artusi.

Key Qualifications:

MSc degree required in a relevant field (computer engineering, computer science, image processing, computer vision, computer graphics etc.). Exceptional candidates holding a BSc may also be considered.
Experience in Image Processing and/or Computer Vision and/or Computer Graphics
Strong computer programming skills (e.g. C/C++, Pyhton, Matlab)
Knowledge of Deep-learning and usage of one of the existing Deep-learning environments (e.g. Keras, Tensorflow, Pytorch etc.)
High-impact research publications on relevant topics (a must for PhD holders/an advantage for non-PhD holders)
Previous experience with embedded systems, i.e., Jetson Nvidia module, is considered an advantage
Previous relevant industrial experience will be considered an advantage
Excellent knowledge of English language
For non-EU applicants a work permit will be required.

Benefits:
A very attractive remuneration package will be offered to successful candidates according to qualifications and experience, including membership to the RISE Employee Medical Scheme and RISE Provident Fund.

Application Process:
Interested candidates should submit the following items via email to vacancies@rise.org.cy and use the Email subject line: "RA Software Developer DeepCamera".

The position will remain open util filled.

Outrider, the pioneer in autonomous yard operations for logistics hubs, helps large enterprises improve safety, increase efficiency, and optimize their workforce. The only company exclusively focused on automating all aspects of yard operations, Outrider eliminates manual tasks that are hazardous and repetitive. Outrider’s mission is to drive the rapid adoption of sustainable freight transportation by deploying zero-emission systems. Outrider is a private company backed by NEA, 8VC, and other top-tier investors.

Required qualifications

Masters degree in computer science or relevant field with exposure to classic and modern computer vision techniques
3+ years of professional C++ and Python experience
Expertise training, evaluating, and deploying models with a deep learning framework such as PyTorch or Tensorflow
Experience working on a team in a Linux environment and targeting embedded deployment
Excellent written and verbal communication skills
Exceptional analytical skills
Demonstrated strong leadership and people skills
Sterling references

Ideal qualifications

AWS experience with S3, SQS, Lambda, DynamoDB, and EC2
ROS / software for ground robotic systems
Experience with embedded computer vision
Prior experience designing annotation ontologies and working with data labeling vendors
Familiarity with as many of the following: stereo vision, LIDAR, radar, and thermal sensing technology
Prior use of Git for software version control
FOSS libraries/frameworks such as OpenCV, the Point Cloud Library (PCL), and similar packages
FOSS tools supporting software engineering, such as CMake, continuous integration packages, the Google test framework and others
PhD and relevant publications and patents

Institute of Big Data, located in Hanoi, is a research institute funded by Vingroup - the largest private enterprise in Vietnam. Our mission is to conduct data-driven research that helps to develop high-impact applications. We are currently looking for Computer Vision Researcher/Engineer to participate in building models, develop products.

Job description:
- Experiment and implement computer vision algorithms, machine learning techniques to solve real-life problems (face recognition, human detection, emotion detection, text recognition…);
- Build the pipeline to manage, process and maintain big data
- Propose, build and optimize the implementation of solutions;
- Support implementation of the above solutions into practical projects.

Requirements:
- Understand the base and architecture of deep learning models.
- Know the Python code, able to quickly learn new languages if needed;
- Prioritize candidates with experience in deep learning frameworks (Tensorflow, Pytorch...), OpenCV and image processing;
- Prioritize candidates with experience in OCR (Optical Character Recognition) and human detection.
- Prioritize candidates with good algorithm and math understanding.

Common interests:
- Very competitive salary
- Private health insurance cover for VinMec and top hospitals in VN
- The preferences when using the services of the Vingroup’s member companies: such as VinFast, VinMart/VinMart+, VinPearl, VinMec, VinSchool, VinID, etc.
- Training course and certificates support
- Team building/company trips
- Opportunities to join a diverse and talented team
- And many more!

Title - Efficient Machine Learning-Enhanced Modeling and Simulation on Exascale Architectures
Duration - 3 yrs
Salary - ~55,000 USD per annum

This postdoc project aims at improving the efficiency and performance of scientific modeling and simulations on exascale architectures through machine learning-enabled adaptivity, providing scalable, robust solutions with guaranteed accuracy in the least amount of time. Adaptivity is essential to increase the automation of the modeling and simulation workflow and to address the growing complexity of applications and architectures. Exascale architectures rely on intricate interplay between thousands of heterogeneous processing nodes, each with a large number of cores, accelerators, memory types, and sophisticated interconnects. As a result, choosing optimal algorithms and implementations is highly application- and architecture-dependent. This project will investigate the opportunities of:

leveraging scientific machine learning (SciML) in developing new algorithms, data layouts, and implementations that dynamically optimize the use of computational resources of exascale architectures, and
leveraging high-performance scientific computing (HPC) in developing scalable and efficient machine learning training algorithms on exascale architectures.
Smart power systems with renewable energy will be an application domain in this project.
official application link - https://www.jobbnorge.no/en/available-jobs/job/183137/postdoctoral-rese…

Title - “Scalable machine learning for online forecast and control of ventilation resources in operation theaters – SmartVentilate”.
Duration - 4 years.
Salary - ~ USD 47,000 per annum
Deadline - March 2020
Contact person – Assoc. Prof. Dilip K. Prasad (dilip.prasad@uit.no)

Description - Large-scale analysis based on big data is crucial for scientific discovery and societal digitalization. The large-scale analysis has driven the development of scalable and adaptive machine learning methods to automatically process large amounts of data that have to be stored on many machines. Although scalable machine learning methods available today (e.g., deep learning) can provide high prediction accuracy, they provide little knowledge and insights into the resulting models. Moreover, they use rigid architectures which restrict their adaptability to the changing needs of learning.
This project will devise novel scalable machine learning methods and systems for which the use case is to pertain continuous and online control of ventilation for patients in operation theaters and intensive care units that are dependent partially or completely on assisted ventilation. Large hospitals such as UNN typically have individual ventilation units per patient as well as distributed supply of oxygen and other resources for ventilation units. This project will investigate and develop new machine learning methods that scale on modern extreme-scale computing systems, adapt themselves to the need of learning, and facilitate interpretation. The theoretical foundations of machine learning will be re-examined to develop new learning methods with improved interpretability and adaptability.

Job post link (application strictly via Jobnorge) - https://www.jobbnorge.no/en/available-jobs/job/183255/phd-fellow-in-com…

GrAI Matter Labs (www.graimatterlabs.ai), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation.

We are looking for R&D Engineer - Machine Learning to join our Architecture team based in Eindhoven. In this role you will develop the Algorithms and Machine Learning solutions that will drive and exercise the revolutionary AI engines embedded in our product Systems on Chip (SoC).

In collaboration with Marketing and Applications teams, you will be responsible for designing, implementing and optimizing domain-specific ML models adapted to the neuromorphic compute architectures.

You will be working on exciting and emerging topics, such as neuromorphic model design, compression, quantization, sparse activation, and efficient hardware implementations. You will be pioneering the development and implementation of neuromorphic computational models and algorithms based on the state-of-the-art machine learning understanding.

The Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Germany, are looking for a highly motivated Postdoctoral Researcher with a strong record of accomplishment in machine learning and computer vision. Successful candidate will conduct research and develop advanced deep learning based algorithms for satellite image search/retrieval from large-scale data archives and semantic scene understanding. This will entail the development of novel deep learning models that can address the problems on incomplete, noisy and imbalanced training sets for scalable image search, retrieval and classification. This research activity is a part of the ERC-funded project: BigEarth - Accurate and Scalable Processing of Big Data in Earth Observation.

As a software engineer on the Autopilot Computer Vision and AI team, you will contribute to one of the most advanced and widely-deployed computer vision stacks in the world. Along with top researchers from academia and some of the most experienced autonomous vehicle engineers in the industry, you will marry cutting-edge deep learning algorithms with robust, real-time software, and deliver safety-critical features to hundreds of thousands of customers. You will develop and support a host of different projects, driven first-and-foremost by our mission to deploy the safest and most effective product in the market.

Responsibilities

Develop real-time, embedded C++ software to decode, interpret, and assemble the raw neural network outputs into a form consumable by the planning and control stack.
You will build and employ a variety of tools for visualizing, debugging, and validating various layers in the vision pipeline.
You will compose algorithms, primarily in Python, to process massive amounts of fleet data for offline processing.
You will work closely with clients of the vision stack to ensure API’s are sufficient, signal quality and gaps are well-understood, and future needs are being anticipated.

Requirements

MS or PhD in Computer Science, Physics, Electrical Engineering or proof of exceptional skills in related fields, with practical software engineering experience.
Minimum 3 years of experience writing production-level C/C++; experience with C++11 (and later), real-time systems, and generic programming are highly desirable.
Mathematical fundamentals, including: linear algebra, vector calculus, probability, and statistics. Experience implementing this math effectively in software (eg MATLAB, Python, numpy, C++/Eigen, etc.).
Familiarity with core problems in robotics, including state estimation (Kalman filter, particle filter, etc.), SLAM, and signal processing (LTI filtering, outlier rejection, reasoning in both time and frequency domains).
Familiarity with basic computer vision concepts, including: intrinsic and extrinsic calibrations, homogeneous coordinates, projection matrices, and epipolar geometry. Some additional expertise in more advanced geometric fields, such as 3D reconstruction, structure from motion, visual odometry, etc., is highly desirable.
Experience working in a Linux environment.
Basic Git knowledge: creating and merging branches, cherry-picking commits, examining the diff between two hashes. More advanced Git usage is a plus, particularly: development on feature-specific branches, squashing and rebasing commits, and breaking large changes into small, easily-digestible diffs.

Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV, and Amazon Echo. What will you help us create?

The Role:
We are a smart team of doers that work passionately to apply cutting-edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. The team is using computer vision, machine learning, sensor fusion, real-time and distributed systems to convert requirements into concrete deliverables. A Researcher on this team will translate business and functional requirements into working code. Comfort with a high degree of ambiguity and ability to solve problems that haven’t been solved to scale before are essential.

In this role, you will work closely with other researchers in the team to research, design, and deliver science components powering computer vision and active learning algorithms to cost-effectively create very large-scale training datasets for various compute vision/robotics applications. You will have the unique opportunity to work on multiple popular Computer Vision tasks altogether, therefore, embrace the great potential to innovate more. You will also be working on developing new algorithms to improve deep learning models including reducing the sample complexity, improving generalization ability, boost model robustness, etc. You will be a critical science contributor and can create a significant business impact to the company. This opportunity requires excellent technical, problem-solving, and communication skills. An ideal candidate will have a high teamwork mentality coupled with a strong bias for action yet always insisting on the highest standards.

Frankfurt Big Data Lab:
http://www.bigdata.uni-frankfurt.de

Open Position: Research Scientist – AI and Ethics.

The Frankfurt Big Data Lab is looking for an outstanding and motivated researcher to join the strong network of researchers working on the definition of an assessment process for Ethical AI, called Z-inspection.

Z-inspection is being currently developed by the team of Prof. Zicari at the Frankfurt Big Data Lab and it could be part of an Ethics by Design process, or if the AI has already been designed, it can be used to do an ethical sanity check, so that a certain AI Ethical standard of care is achieved. It can be used by a variety of AI stakeholders.

Z-inspection is an open development and incremental improvement to establish our process and brand (“Z Inspected”). The project is interdisciplinary in its nature.

More info on our research work on AI and Ethics is available here:

http://www.bigdata.uni-frankfurt.de/z-inspection-process-assess-ethical…

http://www.bigdata.uni-frankfurt.de/ethics-artificial-intelligence/

This is our team: http://www.bigdata.uni-frankfurt.de/people/

The candidate will also help teaching Z-inspection in classes and help at the Summer School on AI and Ethics we will organize in August 2020: https://summerschool.uni-frankfurt.de/portfolio-items/ethical-implicati…

Pre-requisites
Applicants should have a PhD (or being in process of completing a PhD) in a subject that is relevant to the research area, a strong research track record, expertise in Machine Learning, an interest in AI and Ethics, and fluent written and verbal communication skills in English.

Salary
The salary for Research Scientist (WIMi) is based on the department’s salary scale.

Starting date
By agreement

Appointment time
The position is a full time research position (WiMi) with duration till March 30, 2021.

Applications, written in English, should include:

- Curriculum Vitae (including a publication list and three referees)

Please send your application by mail to roberto@zicari.de

Deadline for the application is March 30, 2020

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Committed to People, Committed to the Future. The Toshiba Group is an ambitious and forward-thinking brand, which is recognised for its history of world leading manufacturing and design of advanced electronics and information systems. We develop reliable technologies in the area of social infrastructure, energy, electronic devices and digital solutions with the aim of achieving a sustainable tomorrow. The Cambridge Research Laboratory is a vital part of Toshiba’s worldwide research and development network, as it is responsible for a number of significant world-first developments, and the results of its research are already finding their way into new Toshiba products.

Our staff are our greatest asset. We are an equal opportunity employer, recruiting the most talented researchers and allow them to work in a well-resourced, multicultural and academic environment to promote independent thought and creativity. Publishing at major conferences and in top journals is our main objective, and you will be able to actively contribute to our collaborations with Oxford and Cambridge Universities (Associate Membership is also possible), and the Toshiba Corporate R&D Center in Japan.

Apply by 22nd March 2020

30 PhD positions in Digitally-Enhanced Reality

Deadline: Rolling deadline, first call closing at 17.00 (Irish time) on Wednesday 18th March 2020.
Scholarship: Full payment of university fees and a tax-free stipend of €18,500 per annum for four years. In addition, a budget for conference travel, equipment, training, placement maintenance and publication costs is provided.
Positions: These 30 positions are being advertised as part of the Science Foundation Ireland-funded Centre for Research Training in Digitally-Enhanced Reality (D-REAL). Successful applicants will form a cohort-based doctoral programme involving five leading universities in Ireland –Dublin City University, NUI Galway, Trinity College Dublin, Technological University Dublin and University College Dublin. The programme has a supervision team of 100+ academics and there will be four cohorts over the lifetime of the project. We are currently recruiting for cohort 2.
About the programme: Multimodal digital media, across video, text, image, speech and Virtual/Augmented Reality (VR/AR) content, are rapidly reshaping our working and living environments. Seamlessly blending digital media and interaction within the physical world offers disruptive potential to enhance our effectiveness, efficiency and quality of engagement in everyday life. The D-REAL programme is an innovative, industry partnered, research training programme that equips PhD students with deep ICT knowledge and skills across Digital Platform Technology, Content and Media Technology and their application in Industry sectors. D-REAL postgraduate students will make research breakthroughs in areas such as multimodal interaction, multimodal digital assistants, multilingual speech processing, real-time multilingual translation and interaction, machine intelligence for video analytics and multimodal personalisation and agency.
Topics: The D-REAL website lists the 30 available projects, giving details on the supervision team and a brief descriptor of the scope of the PhD project. You can view the topics at this link: d-real.ie/d-real-2020-phd-topics/
Minimum requirements:
2.1 grade (or equivalent) in an undergraduate or postgraduate degree in computer science, maths, engineering or similar technical discipline. Other qualifications in disciplines related to listed PhD topics will also be considered.
Strong programming ability.
Non-native English speakers require at least IELTS 6.5 (with at least 6 in all components) or equivalent.
Application procedure: Applications are to be made using the form provided on our ‘Apply to D-REAL’ webpage. You will be asked for personal details, academic track record, a personal statement and to list your top three topic preferences.
Queries: For queries about the programme please contact the programme manager, Stephen.Carroll@d-real.ie. For queries about the projects themselves please contact the primary supervisor of the particular project.

Two post-doctoral scholar positions are available at the Vanderbilt Dermatology Translational Research Clinic (VDTRC.org) and the Vanderbilt University School of Engineering to launch translational research careers. We invite motivated candidates to apply their backgrounds in engineering, physics, and/or computer science to medical problems facing oncology patients. The goal is to develop, commercialize, and deploy technologies in our ongoing multicenter trials to track disease progression and response to treatment following stem cell / bone marrow transplantation. Specific projects are:

1. SKIN MECHANICS. The scholar will develop and validate a novel handheld clinical device to mechanically measure cutaneous sclerosis. This project will involve signal processing of mechanical vibrations, validation with ultrasound tissue phantoms, and clinical measurement. Our prior results: https://www.nature.com/articles/s41409-018-0346-7

2. ANALYSIS OF RASH IMAGES from smartphone and hyperspectral cameras. The scholar will analyze existing 2D and 3D skin images and advise prospective collection from collaborating centers. Technical approaches may include image registration algorithms, traditional image processing, crowd sourcing, and artificial intelligence such as clustering algorithms and deep learning for segmentation and colorimetric analyses. Our prior results: https://www.nature.com/articles/s41409-018-0211-8

Selected candidates will take the lead on the funded project and also will be encouraged to formulate their own research ideas. They will be provided infrastructure and mentoring for independent funding. Scholars will interact with a broad range of collaborating experts in clinical medicine and technology as well as several innovative partners in industry. Trainees in this well-funded program will enjoy competitive benefits including NIH rate salary.

Environment:
Selected candidates will benefit from a personalized research program catered to their goals utilizing resources and mentoring within Dermatology (Eric Tkaczyk), Computer Science (Benoit Dawant), Bioinformatics (Daniel Fabbri), Biomedical Engineering (Brett Byram and Anita Mahadevan-Jansen), and the Vanderbilt-Ingram Cancer Center (Madan Jagasia). This environment brings together clinicians, basic scientists, engineers and statisticians in close collaboration due to its interdisciplinary research and close physical proximity of the School of Medicine to the School of Engineering (100-200 yards).

Qualifications:
Applicants must have a demonstrated record of rigorous & creative contributions and a capacity to communicate effectively with experts from a range of disciplines. Interviews are underway, with the first position to be filled by summer 2020. The Vanderbilt University Medical Center is an equal opportunities employer.

Application and contact:
Every applicant should send a CV, a one-page personal statement, and two letters of recommendation with reference phone numbers and email addresses to:

Eric Tkaczyk, M.D., Ph.D.
Director, VDTRC (vdtrc.org)
Vanderbilt Dermatology
One Hundred Oaks Suite 26300
719 Thompson Lane
Nashville, TN 37204
VCICrecruit@gmail.com

Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV, and Amazon Echo. What will you help us create?

The Role:
We are a smart team of doers that work passionately to apply cutting-edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. The team is using computer vision, machine learning, sensor fusion, real-time and distributed systems to convert requirements into concrete deliverables. A Researcher on this team will translate business and functional requirements into working code. Comfort with a high degree of ambiguity and ability to solve problems that haven’t been solved to scale before are essential.

In this role, you will work closely with other researchers in the team to research, design, and deliver science components powering computer vision and active learning algorithms to cost-effectively create very large-scale training datasets for various compute vision/robotics applications. You will have the unique opportunity to work on multiple popular Computer Vision tasks altogether, therefore, embrace the great potential to innovate more. You will also be working on developing new algorithms to improve deep learning models including reducing the sample complexity, improving generalization ability, boost model robustness, etc. You will be a critical science contributor and can create a significant business impact to the company. This opportunity requires excellent technical, problem-solving, and communication skills. An ideal candidate will have a high teamwork mentality coupled with a strong bias for action yet always insisting on the highest standards.

Vingroup Big Data Institute (VinBDI) is looking for a Research Scientist to join our Medical Imaging Team. This position aims to build large-scale and high-precision medical imaging solutions to facilitate effective clinical workflows. We are passionate about applying Computer Vision (CV), Machine Learning (ML) and Deep Learning (DL) models to build computed-aided detection (CAD) and computer aided diagnosis (CADx) systems from very large-scale clinical datasets of multiple imaging modalities (X-ray, CT, MRI, etc). After building prototypes that demonstrate the promise of your work, you will collaborate with the software engineering team to integrate your work into products as well as publish the work on top-tier journal/conferences.

What You'll Be Doing

Design and implement CV/ML/DL approaches to solving particular medical imaging problems related to detection, segmentation and classification in medical imaging.

Construct and normalize large-scale medical datasets (X-ray, CT, MRI, etc).

Keep up with the latest ML/DL research.

Produce top tier technical/clinical publications and transfer ML/DL models into products.

What We Need To See

A Ph.D. in Electrical Engineering, Computer Science, or a related field with 5+ years of relevant work experience in Medical Imaging, CV/ML/DL.

Publication records on top CV/ML/DL journals/conference and/or significant product development.

Excellent prototyping skills in Python; knowledge and development experience of common deep learning frameworks and packages (PyTorch, TensorFlow, Keras, etc.).

Deep experience in application areas such as medical imaging and computer vision;

Knowledge of medical imaging visualisation and PACs is a big plus.

We are seeking a PhD candidate to work at the ATHENA Research Center, in cooperation with the Department of Informatics & Telecommunications of the University of Athens, Greece. The PhD will be co-advised by Ioannis Emiris (https://www.imis.athena-innovation.gr/en/people/member/64) and Yannis Avrithis (https://avrithis.net/). The position is for three years, starting in 2020.

The PhD aims at investigating and designing deep learning models to embed 3D shapes in vector spaces, using novel, powerful architectures and weak supervision settings. In particular, it will investigate methods operating on 3D point clouds directly by generalizing convolution to 3D while maintaining a sparse representation, not necessarily on the input point set. It will also explore settings including self-supervised, semi-supervised, weakly supervised, few-shot and incremental learning, allowing the use of unlabeled 3D data. Tasks to be investigated include classification, detection, segmentation and metric learning for 3D shape retrieval.

The PhD position is in the framework of GRAPES: Learning, Processing and Optimising Shapes, a Marie Sklodowska-Curie European Network (http://grapes-network.eu/).

The candidate should ideally have a Master's degree in Computer Science, Mathematics or Engineering; solid mathematical background and programming skills; fluency in English language; preferably, prior experience in computer vision, machine learning and deep learning.

Application instructions can be found at http://grapes-network.eu/phd-positions/how-to-apply/. In particular, candidates should satisfy eligibility conditions including trans-national mobility and being Early Stage Researchers (ESR).

Candidates are requested to send an initial expression of interest to Ioannis Emiris (emiris@athena-innovation.gr) and Yannis Avrithis (ioannis.avrithis@inria.fr) by March 10, 2020. Applications are reviewed continuously until the position is filled. The target starting date is September 1, 2020.

Position Overview:
This is an interdisciplinary position at the intersection of artificial intelligence, deep learning, and transportation systems. The Ph.D. student will work on research projects focused on developing innovative machine learning solutions for optimizing the operations of an electric autonomous vehicle-based transit system such as inferring trip characteristics, modeling trip choices, placement of charging stations along with reinforcement learning for optimizing the choice of system parameters.

The student will primarily be admitted to the Department of Electrical and Computer Engineering. The project will be conducted jointly between the Manitoba Learning and Artificial Intelligence Research (MLAIR) lab in the Department of Electrical and Computer Engineering and the Transportation Systems Research Lab in the Department of Civil Engineering at The University of Manitoba in Winnipeg (MB), Canada.

SUMMARY OF JOB FUNCTIONS:
• Algorithm design and development for machine learning, and statistical modeling solutions.
• Design and Development of Artificial Intelligence and Deep Reinforcement Learning-driven Solutions for Transportation Systems.
• Perform necessary quality control procedures to ensure accuracy and completeness of the research data.
• Preparing scientific manuscripts and reports.

QUALIFICATIONS AND EDUCATION REQUIREMENTS
• Masters in Computer Science, Computer Engineering, Electrical Engineering or related fields.
• Strong background in machine (deep) learning and statistical analysis.
• Strong background in classical and deep reinforcement learning.
• Strong scientific programming skills with Python; Matlab and Fortran are an asset.
• Facility with deep learning frameworks such as PyTorch, Keras, and Tensorflow.
• Hands-on in virtual environments.
• Proven research-based publications in deep learning.
• Excellent technical writing for scientific publications.
• A minimum G.P.A. of 3.5/4.5 or equivalent in your last 60 credit hours of study is required.
• A minimum IELTS score of 7.0 is required.
• Excellent problem-solving skills.
• Ability to work well with a team and to use initiative in achieving goals.
• Experience with reinforcement learning for transportation systems, simulation of traffic flow is a plus. Candidates with excellent experiences in reinforcement learning for other domains are encouraged to apply.

DEPARTMENT DESCRIPTION
At the Manitoba Learning and Artificial Intelligence Research (MLAIR) lab, we conduct high impact research in developing novel artificial intelligence and deep learning architectures for multimodality data such as imaging, computer vision, robotics, machine learning and data driven discovery of radiogenomic markers of disease progression, hybrid neural architectures for multi-format, multi-source spatiotemporal imaging data.

Research at the Transportation Systems Research Lab focuses on establishing interdisciplinary and collaborative research related to traffic flow modeling and simulation, adaptive traffic control, modeling and analysis of public transportation systems and transportation network analysis.

Please email Ahmed.Ashraf@umanitoba.ca for more details.

MLAIR Lab: https://home.cc.umanitoba.ca/~ashrafa/

Job Post Link: https://home.cc.umanitoba.ca/~ashrafa/positions/Ph.D._DLRL.pdf

The Applied Research and Technology team for Photoshop and Digital Imaging is looking for a passionate applied research engineer to do research and development on building imaging and photography experiences that delight customers. This individual will work with a very multifaceted customer-focused engineering team and will be responsible for productizing research ideas. The successful candidate will help define and build “Adobe magic” in digital imaging applications for multiple platforms.

What you will do
Research and develop deep learning models to tackle computer vision and imaging processing problems. Provide real-world imaging solutions from state-of-the-art research and drive efforts to productize the solutions.
Build complete pipelines combining traditional computer vision algorithms and techniques with machine learning models to address a wide range of use cases and customer needs.
Collaborate with researchers, team members, UI designers, product managers, quality engineers and customers to convert research projects into polished features with phenomenal user experiences
Develop end-to-end solutions from back-end algorithms to front-end user experience
Come up with new and innovative ideas to implement new imaging features that will work well across desktop, mobile and cloud environments.
Maintain, evolve and improve existing features in a large, complex codebase
Identify mature research ideas and prototype for proof of concept.
Write design and architectural specs.
Participate in formal and informal code and design reviews.
Serve as a customer advocate, champion quality.
Bring energy, flexibility, focus and perfection to all projects and assignments.
Take initiative, seek new challenges and acquire necessary knowledge and skills to get the job done.

What you need to succeed
PhD in Computer Science or related field, with computer vision or machine learning emphasis preferable.
2+ years of software development experience with proven track record of designing, implementing and delivering high quality software.
Good blend of research and engineering skills to solve complex problems for shippable solutions.
Experience with deep learning technologies and frameworks required.
Solid foundation in computer science fundamentals, object-oriented programming, design patterns and software engineering best practices.
Ability to write efficient, clean and reusable code in Python and C++.
Computer vision and image-processing knowledge and experience preferred
Working knowledge and experience developing for multiple platforms including desktop, iOS and Android preferred.
Dedication to excellent coding practices.
Outstanding problem-solving & analytical skills.
Accustomed to working with large and complex software systems.
Outstanding written and verbal communication skills.
Passion for building high quality applications users love.
At Adobe, you will be immersed in an exceptional work environment that is recognized throughout the world on Best Companies lists. You will also be surrounded by colleagues who are committed to helping each other grow through our unique Check-In approach where ongoing feedback flows freely.

If you’re looking to make an impact, Adobe's the place for you. Discover what our employees are saying about their career experiences on the Adobe Life blog and explore the meaningful benefits we offer.

Adobe is an equal opportunity employer. We welcome and encourage diversity in the workplace regardless of race, gender, religion, age, sexual orientation, gender identity, disability or veteran status.

Job Description:
PENSEES Research Institute in Beijing and Singapore invites suitably qualified persons for the above position. As a Post-Doctoral Research Assistant, your wish is to embark on a career as an AI Algorithm Scientist. You will be involved in the research and development of various fundamental and cutting-edge machine learning (“ML”), deep learning (“DL”), computer vision algorithms that advances facial recognition software as well as the development of algorithm libraries for platform deployment.

Job Responsibilities:
As a Post-Doctoral Research Assistant, you will be responsible to:
1) Design and implement ML/DL/Computer Vision Algorithm for various types of application data and business cases, which includes facial recognition, smart community and enterprise security;
2) Assist in the migration, optimisation, maintenance and testing of algorithm modules for real applications;
3) Contribute to the life cycle of models from proof-of-concept, to scaling the training, to inference deployment at scale, using Pensees novel hardware and working closely with AI scientists to drive systems from prototyping to production level;
4) Involve in the submission of papers, taking part in research conferences and competitions.

Job Requirement:
To be considered for this position, you will need to have:
• PhD Degree in the area of Machine Learning, Computer Vision or Pattern Recognition;
• 1 - 3+ years of experience in DL, ML algorithms and model development;
• Proficiency in the use of programming languages such as C/C++/Python/Java, familiar with data structures, familiar with Caffe/Tensorflow/MXnet/DarkNet, algorithm design and implementation, and experience in the full development cycle from algorithm research to modelling, data preparation, test, evaluation and implementation in live environments;
• Strong analytical and strategic thinking skills to handle both the big picture and crucial technical decisions;
• Strong desire to follow the latest technological developments to implement cutting-edge algorithms for business applications;
• Good communication skills, proactive attitude and passionate about new technologies and ideas;
• Priority will be given to those with publications and awards in international competitions and top tier conferences and journals for algorithms and ML/DL/computer vision research and development. Experience in facial recognition and Computer Vision is preferred.

How to Apply?
If this sounds like you and you are looking for a new and exciting challenge, apply today to: hr.sg@pensees.ai. Our Chief Scientist will also be at CVPR 2020 and you can send your CV and contact her at jane.shen@pensees.ai. Find out more about Pensees at www.pensees.ai