Job Board

Founded by Dr. Yuanqing LIN in November 2017, Aibee is a leading AI startup providing AI full solutions, and its mission has been to empower and upgrade vertical industries with AI. We are a leading company in the application of AI technologies for retail, has developed full AI solutions for offline retailers that allow closing the loop among data, algorithms, technologies, and product/service. Aibee applies the most advanced and innovative AI technologies (computer vision, speech recognition, natural language understanding, big data analytics, robotics, etc.) and has become the strategic partner of many of the world’s largest retail chains.

We are looking to grow our Palo Alto team with talented software engineers, develop software and models that marries cutting-edge computer vision and deep learning technology.

Responsibilities:
• Architect, build and maintain large scale distributed systems to support the whole ML pipeline from data collection and training to deployment
• Design, develop, and maintain distributed software systems that incorporate real-time and streaming data.
• Characterize complex problems related to the scalability, reliability, performance, and security of production systems.
• Collaborate with cross-functional engineering and research teams in computer vision and machine learning.
Qualifications:
• MS or PhD degree in Computer Science or related technical field.
• A strong foundation in computer science, with strong competencies in data structures, algorithms, distributed systems, and software design.
• Experience in building large scale production data pipelines.
• 3+ years of experience engineering in C++ or Python.
• Familiarity with machine learning frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks is a plus
• Experience delivering software and systems that support Machine Learning, Deep Learning, or both is a plus
Benefits:
• Comprehensive Medical, Dental and Vision Insurance
• Retirement Plan
• Generous PTO and Paid Holidays
• Daily Meal and Bi-weekly Happy Hours
• Flexible WFH Policy
• Frequent Tech Talks

Founded by Dr. Yuanqing LIN in November 2017, Aibee is a leading AI startup providing AI solutions, and its mission has been to empower and upgrade the vertical market with AI. We are a leading company in the application of AI technologies for the retail industry, have developed full AI solutions for offline retailers that enable them to close the loop among data, algorithms, and product/service. Aibee applies the most advanced and innovative AI technologies (computer vision, speech recognition, natural language understanding, big data analytics, robotics, etc.) and has become the strategic partner of many of the world’s largest retail chains.

We are looking to grow our team with talented algorithm engineers, develop software and models that marries cutting-edge computer vision and deep learning technology.

Responsibility:
· Research, design, develop, implement and optimize algorithms related to the broad problems of computer vision, including but not limited to pose estimation/tracking, object detection, tracking, image/video semantic segmentation, video semantic understanding, , 3D vision, etc.;
· Analyze, track, and produce state-of-the-art research related to Aibee AI solutions;
· Optimize computer vision algorithms and apply to real applications.
· Implement production-quality algorithms with strong ownership.
· Explore promising research and maintain/gain the technical edge required for Aibee AI projects; share and develop new approaches and methods.

Qualifications:
· MS (or BS with at least 3 years working experience) in Computer Science, Electrical Engineering, Computational and Mathematical Engineering, or a closely related field
· 3+ years of experience in C++ or Python, for computer vision or machine learning applications.
· Knowledge and experience in computer vision and machine learning technologies.
· Solid understanding of basic mathematical tools like Linear Algebra and Probabilities.
· Background in machine learning with experience in large scale training and evaluation of deep convolutional and/or recurrent neural networks is preferred.

Benefits:
· Comprehensive Medical, Dental and Vision Insurance
· Retirement Plan
· Generous PTO and Paid Holidays
· Daily Meal and Bi-weekly Happy Hours
· Flexible WFH Policy
· Frequent Tech Talks

Founded by Dr. Yuanqing LIN in November 2017, Aibee is a leading AI startup providing AI solutions, and its mission has been to empower and upgrade the vertical market with AI. We are a leading company in the application of AI technologies for the retail industry, have developed full AI solutions for offline retailers that enable them to close the loop among data, algorithms, and product/service. Aibee applies the most advanced and innovative AI technologies (computer vision, speech recognition, natural language understanding, big data analytics, robotics, etc.) and has become the strategic partner of many of the world’s largest retail chains.

We are expecting top-notch Computer Vision Algorithm Scientists to join our R&D center in Palo Alto (currently remote), work on production level computer vision and deep learning models. We have access to a huge amount of real retail data, enabling researchers like you to develop complex models and techniques at scale.

The problems range from various research domains in Computer Vision and Machine Learning such as:

• Activity Recognition and Event Detection
• (Multi-Camera) Multiple Target Tracking
• Re-identification and Metric Learning
• Holistic visual understanding
• Object Detection, Segmentation, and Classification
• Pose Estimation and Tracking

Preferred Qualifications:
• PhD (or master with at least 3 years working experience) in Computer Science, EE, Applied Mathematics, or related fields.
• Strong publication record in premier AI-related venues such as CVPR, ICCV, ECCV, NeurIPS, or other related major conferences or journals.
• Strong analytical and problem-solving skills.
• Team player with good communication skills.
• Strong coding skills with Python or C++.

Benefits:
• Comprehensive Medical, Dental and Vision Insurance
• Retirement Plan
• Generous PTO and Paid Holidays
• Daily Meal and Bi-weekly Happy Hours
• Flexible WFH Policy
• Frequent Tech Talks

MyWorld is a £46m R&D programme, awarded to the University of Bristol, under the leadership of Professor David Bull. It is a major intervention by UK Government to explore the potential of devolved R&D funding. There are now a number of opportunities (up to 9 posts) for outstanding candidates to join the MyWorld team as PhD students, who are expected to start from Sept 2022. Opportunities for innovation and investigation exist across the MyWorld portfolio, including content acquisition and post-production, content delivery and interactivity, and audience understanding.

All posts will cover student stipend at a basic rate of £15,609 per annum (2022 rates) with possibility of enhancement by up by £3,000 in some cases. Fees for home (UK-based) students are covered in all cases. Several awards cover fees for EU students and some cover overseas students. Appointees will be expected to integrate within the MyWorld team, to conduct internationally-leading research, and to contribute to the wider objectives and activities of the programme. Many of the awards will involve collaboration with our industry partners and would offer the potential of career development through internships as part of the PhD.

The research focus of these PhD studentships will be linked to the strategic objectives of MyWorld, promoting new technology research that underpins the delivery of future experiences and services. Applications are invited in the following areas:

• Content Acquisition and Post-Production (up to 3 posts): AI methods in post-production – video denoising, colorisation and enhancement; low light fusion and autofocus (BBC iCASE sponsored); virtual production technologies; intelligent and automated cinematographies (including drone cinematography); camera tracking and SLAM methods in virtual production; Building interactive worlds – enabling the metaverse; creating re-useable assets for virtual production.
• Content Delivery and Interactivity (up to 3 posts): perceptually optimised video compression; dynamic optimisation of streamed video; energy-efficient video coding; new architectures and tools for emerging AoM standards (Netflix sponsored); machine learning methods for video delivery; perceptual video quality metrics; transcoding methods for user generated content; volumetric video coding (BT iCASE sponsored); coding beyond compression, media network optimisation.
• Audience Understanding (2 posts): Methods for assessing quality of experience and immersion; biometrics, and fusion of these, for audience understanding; motion magnification for user engagement; creation and exploitation of visual field maps.
• Experimental Productions (1 post): Enabling the metaverse; building environments for virtual rehearsal; building and evaluating immersive natural history experiences.

For an informal discussion about the scholarships, please contact:

• Professor David Bull, Director MyWorld, Director Bristol Vision Institute (All projects): dave.bull@bristol.ac.uk
• Dr. Aaron Zhang, Lecturer in Visual Communications (Content delivery projects): Fan.zhang@bristol.ac.uk
• Dr. Pui Anantrasirichai, Senior Lecturer in Creative Technologies (Content acquisition and post-production projects): n.anantrasirichai@bristol.ac.uk
• Professor Andrew Calway, Professor of Computer Vision (SLAM and tracking for Virtual Production): andrew.calway@bristol.ac.uk
• Prof Dimitra Simeonidou, Head of Smart Internet Laboratory and Director BDFI (Networks): dimitra.simeonidou@bristol.ac.uk
• Prof. Iain Gilchrist, Professor of Neuropsychology (Audience understanding projects): i.d.gilchrist@bristol.ac.uk
• Prof Kirsten Cater, Professor of Human Computer Interaction (Experimental production related projects): kirsten.cater@bristol.ac.uk

All candidates should submit a full CV and covering letter to myworldrecruitment@myworld-creates.com (FAO: the contact of the research topic that you are applying for).

Climate extremes are on the rise. This is one of the most critical manifestations of climate change as extreme events have multiple impacts: from declining ecosystem functioning associated with reduced ecosystem services e.g., carbon sequestration and water retention, to harvest failure with very direct impacts on human wellbeing. In the last few years, it has been recognized that the highest threats on ecosystems and societies are due to multi-hazard events. Such events may translate into “compound events”, which often do not only affect one particular land-surface process but rather induce entire cascades of consequences. In the project we will rely on deep learning to deal with spatio-temporal data, techniques from computer vision for forecasting impacts, and the advanced regression methods for associating impacts on biosphere and society. Understanding what the DL models have learned are of importance here, so experience on explainable AI (XAI) techniques and methods from modern Bayesian inference (Bayesian deep learning and deep GP regression) to perform uncertainty quantification, automatic variable relevance determination, and error propagation are pluses.

The Role:

Tesla is on a path to build humanoid bi-pedal robots at scale to automate repetitive and boring tasks for manufacturing/logistics. Core to the Tesla Bot, the deep learning stack presents a unique opportunity to work on state-of-the-art neural network algorithms for deep learning culminating in their deployment to real world production applications. Our deep learning research scientists and engineers develop and own this stack from inception to deployment.

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, dense depth estimation, 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

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

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 code/papers, implementing described models and algorithms, adapting them to our setting, driving up internal metrics, working with downstream engineers to integrate neural networks to run efficiently in the car on our chip, and incrementally tracking and improving feature performance based on fleet telemetry. 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 Natural Science Division of Pepperdine University’s Seaver College of Letters, Arts, and Sciences, located in Malibu, California invites applications for a Visiting Assistant Professor - Keck Scholar in Residence - position for the 2022-2023 academic year beginning August 1, 2022. Applicants should have a Ph.D. (or will receive a Ph.D. by July 2022) in computer science or other related computing discipline. We seek outstanding candidates with core expertise in machine learning to join a vibrant cross-disciplinary scientific community of faculty at Seaver College. Well-qualified candidates will be able to contribute to the excellence and vibrancy of the Pepperdine experience through their teaching and research to both Pepperdine and our surrounding community. The newly created data science program invites applications for a one-year, non-renewable scholar in residence position in machine learning starting in August 2022. Applicants must display a strong publication record, as well as excellent teaching skills to prepare the new generation of data scientists.

As a scholar in residence, candidates should have the background and ability to teach a broad range of introductory and advanced computer science courses. Candidates will teach one class each semester (Fall and Spring) and engage in research and scholarly activities with colleagues and undergraduates. Candidates are expected to be able contribute to the development and implementation of the newly established data science minor. Candidates should also have an active and promising research program. Pepperdine has a strong and well-supported program in undergraduate research, and preference will be given to candidates who can actively involve undergraduate students in their research.

Review of candidates' files will begin March 1, 2022 and applications will be accepted until the position is filled. Apply here: https://apply.interfolio.com/102204

Two job openings as PhD student, at the Institute of Computer Science, Faculty of Computer Science and Mathematics at Goethe University Frankfurt. The PIs of the projects are Prof. Visvanathan Ramesh and Prof. Gemma Roig. The duration of the position is for up to 3 years.

The position will involve:

- Developing cutting-edge research work on vision systems and artificial intelligence, with possibility to explore representational learning, continual learning, multi-task learning, applied different domains, such as affective computing, finance and education.

- The outcome of the research pursued is expected to be published in top tier conferences and journals.

Key requirements for the position:

- Master in Computer Science, applied mathematics, electrical engineering or related fields.

- Strong knowledge in machine learning (deep learning, RNN, LSTMs, supervised, semisupervised, self-supervised, continual learning, etc) .

- Excellent programming skills in python, and extensive experience in Tensorflow, pyTorch or related libraries.

- Knowledge of high performance distributed computing with GPUs.

- At least one publication in tier 1 conferences or journals in related topics to computer science.

- Fluent in English (writing, speaking, oral)

Self-motivated candidates with a hands-on, proactive approach and problem-solving skills are strongly encouraged to apply.

Application Instructions:

Applicants are asked to send their documents (motivational letter, CV, 1 publication, diploma and certificates, course list/transcript) to Ulrike Knauf at knauf@informatik.uni-frankfurt.de until 31st of March, in electronic form. Interviews will be held in Frankfurt or conducted electronically.

Postdoctoral position in Deep Learning for Medical Imaging

The Psychiatry Neuroimaging Laboratory (PNL) at Harvard Medical School (HMS) is requesting applications for a post-doctoral position to develop new deep learning methods to solve inverse problems in MR imaging in general and diffusion MRI in particular. Areas of research that will be the focus of the fellow include, but not limited to: new deep learning methods design, robustness under adversarial attacks, generalizability to arbitrary imaging resolution and acquisition parameters as well as missing data in a multi-modal setting. The fellow will have high visibility with application to very large datasets. The research will also involve solving some of the unknown mysteries of the brain and its connectivity in health and disease.

The position will be at the PNL in the Department of Psychiatry and Radiology. The PNL is housed at the Brigham and Women’s Hospital with access to several CPU and GPU clusters and workstations. The fellow will work in a highly multidisciplinary environment consisting of neuroanatomists, psychiatrists, neuroradiologists, neuro-scientists and computational imaging experts. There is a strong potential for personal and career growth at the PNL with the opportunity to collaborate with peers and experts from other fields.

The candidate will have a Ph.D. in electrical engineering, physics or biomedical engineering with first hand experience developing fast code in any of the programming languages like C/C++ or Python.

Interested candidates, please email yogesh@bwh.harvard.edu with your CV and subject line: Post-doctoral candidate .

The candidate is expected to work on 3D perception problems for challenging indoor/outdoor environments with data-efficient deep learning approaches, and develop research-level codes on multi-modality embedded systems. This position has opportunities to deliver real-world impact on sports analysis, autonomous machines, and smart homes.

Minimal Requirement:
1. Ph.D. degree in Computer Science (CS), Electrical Engineering (EE), or Electrical and Computer Engineering (ECE)
2, One first-author paper in one of the following recent (2019-Present) conferences: CVPR, ICCV, ECCV, NeuIPS, AAAI or ICRA

Preferred Experience:
1. Deep learning solutions (Pytorch and OpenCV) on monocular 3D perception tasks for bounding boxes, body meshes, depth, and point clouds.
2. Distillation or compression algorithms for real-time inference on multi-modality embedded platforms, such as Nvidia Xavier, Xilinx FPGA, or Raspberry Pi.

Official Application URL: https://cmu.wd5.myworkdayjobs.com/CMU/job/Pittsburgh-PA/Postdoctoral-Fe…

Interested applicants are also welcomed to contact the supervisor by email (Dong Huang, donghuang@cmu.edu) with the email subject “Postdoc 2022 at DeLight_YOURNAME”.

Job opening as a postdoc, at Centre for multi-scale modelling, analysis and simulation of biological processes at the Frankfurt Institute for Advance Studies. The PI of the project is Prof. Gemma Roig, and the duration of the position is for up to 2 years.

The position will involve:

- Developing cutting-edge research work on computational models of human visual cognition and artificial intelligence.

- Linking AI models to brain data, including functional MRI, MEG and EEG.

- The outcome of the research pursued is expected to be published in top tier conferences and journals.

Key requirements for the position:

- PhD in Computer Science, applied mathematics, electrical engineering or related fields.

- Strong knowledge in machine learning (deep learning, CNNs, supervised learning, unsupervised learning, self-supervised learning) .

- Excellent programming skills in python, and extensive experience in Tensorflow, pyTorch or related libraries.
- Knowledge of high performance distributed computing with GPUs.

- At least two publications in tier 1 conferences or journals as first author in related topics to computer science and cognitive computational neuroscience.

- Basic knowledge and high motivation to learn about computational neuroscience and cognitive science.

- Fluent in English (writing, speaking, oral)

Self-motivated candidates with a hands-on, proactive approach and problem-solving skills are strongly encouraged to apply.

Application Instructions:

Applicants are asked to send their documents (motivational letter, CV, 2 publications, diploma and certificates) to Gemma Roig at roig@cs.uni-frankfurt.de until 15th of March 2022, in electronic form. Please include in the subject [CMMS postdoc]. Interviews will be held in Frankfurt or conducted electronically.

We are looking for a highly motivated and passionate Machine Learning Engineer to join our team who has expertise in machine learning, computer vision, and software engineering. As a ML Engineer, you will be a part of the interdisciplinary group of research scientists, clinicians, and engineers. In close collaboration with your colleagues, you will help develop, validate, and deploy machine learning models. You will be
responsible for building and maintaining the necessary infrastructure and software pipelines to facilitate this work. You will engage with interdisciplinary research topics and develop machine learning solutions for various complex and novel problems using large scale medical imaging datasets and datasets of other modalities. Leveraging your machine learning knowledge, you will also provide technical machine learning
advice to faculty and lab staff. Lastly, you will operate under standard procedures and protocols in regulatory and compliance. This is a unique opportunity to work in the interdisciplinary area of machine learning and healthcare as you will not only work on developing machine learning models but will also have an opportunity to deploy the models in real clinical workflows, thereby enabling you to make a real world impact by saving and/or improving the lives of real people.

Working field:
The Remote Sensing Image Analysis Group is seeking 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 (https://bigearth.eu)

Requirements:
- Successfully completed university degree (Master, Diplom or equivalent) and PhD degree in computer science, engineering, or mathematics
- Research experience in machine learning for image analysis and computer vision
- Excellent programming skills (e.g., python) with experience using deep learning libraries
- A solid track record of research in machine learning and computer vision journals and/or conferences

1. Mission
Functional capacity evaluation (FCE) is a set of dynamical tests consisting of functional evaluations (sitting, kneeling, standing, bending, climbing a staircase, grasping an object, lifting, carrying, etc.) which provide information on the capacity of a person do act in a work environment. FCE is currently used by employers and medical professionals to decide when an employee is ready to return to work but it can be more broadly used as an indicator of human musculo-skeletal health. We propose to develop an automatic solution for continuous FCE in a domestic environment on the basis of a Computer Vision system for tracking human body posture. Such a system would acquire more temporal data on the musculo-skeletal capacity of a human than a typical doctorat during an FCE session, producing more robust evaluation results.

2. Job environment
The postdoctorate will be located at IMT Atlantique, on its Brest campus, in the RAMBO team (member of Lab-STICC laboratory, mixed research unit of CNRS #6285, https://www.imt-atlantique.fr/en/research-innovation/teams/research-tea…) and supervised by the associate professors Panagiotis Papadakis (https://sites.google.com/site/pgpapadakis) and Mihai Andries (http://mihai.andries.eu/). The research domains of the team include Computer Vision, Cognitive Robotics and Robot Learning, Ambient assisted living and Human-robot interaction. The team possesses a LivingLab for e-Health/Smart Living Experiment’HAAL, equipped with sensors for tracking human activity, where experiments related to the project can be performed, as well as a GERT ageing simulator. This project is part of the RAMBO research activities on ageing-at-home and is complementary to our ongoing research on the recognition of activities of daily living (ADL).

For full details on the subject, the requirements and the application procedure, follow the link "More info" shown below.

UAVs represent one of the most relevant emerging technologies in the remote sensing domain of the last two decades, becoming a valid alternative to traditional acquisition techniques in a wide range of applications. In common practice, UAVs flights are pre-planned before starting the mission while data processing is performed offline after the acquisition. These methods, however, strongly limit the applicability of UAVs in dynamic and complex contexts. Examples of UAVs able to fly autonomously on an interest area, acquire complete data, understand the scene and take autonomous decisions are still in their very early stage. However, these solutions would promise to open new opportunities for more advanced applications.

Your tasks
You will work at the crossroad of photogrammetric computer vision, deep learning and robotics to develop the next generation of algorithms for reliable and autonomous real-time UAV mapping. The research will combine autonomous navigation algorithms with 2D and 3D semantic understanding of the scene to develop the next generation of autonomous drones able to perform complex tasks. Different applications such as patrolling, search and rescue in outdoor and indoor environments, and precision farming will be considered during this period.

Profile
You should have:
- An MSc in computer science or robotics
- Expertise in deep learning
- Excellent programming skills (python, C, Java)
- Proficiency in ROS (optional)
- Experience in customization of drones with the aim of streaming data, etc. (optional)
- Experience with on-board and remote processing (optional)

Our offer
We offer you an inspiring multidisciplinary and challenging international and academic environment. We offer a position for a period of four years. Salary and conditions will be in accordance with the Collective Labor Agreement (CAO-NU) of the Dutch Universities.
- A starting salary of € 2,434.00 in the first year and a salary of € 3,111.00 in the fourth year gross per month;
- A holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%;
- A solid pension scheme;
- A total of 41 holiday days in case of full-time employment;
- Professional and personal development programs;
- Costs for moving to Enschede may be reimbursed.

Information and application
For more information about this position, you can contact Dr. Francesco Nex (f.nex@utwente.nl). You are also invited to visit our homepage.
Please submit your application before 1 March 2021 (choose apply now below). Your application should include:
- A cover letter (maximum 2 pages), emphasizing your specific interest in the position and outlining your relevant skills and experience
- A full Curriculum Vitae

Natural Language Processing (NLP) is a frontier in Artificial Intelligence and Machine Learning. At Adobe Research, we are particularly interested in NLP for multimodal data, such as joint vision and language, video understanding with transcript, and document understanding. Advancing the state-of-the-art in this area is critical to Adobe’s mission of changing the world through digital experience.

We are looking for a Principal Research Scientist in NLP and Multimodal Learning to join a world-class Computer Vision, Deep Learning, and Natural Language Processing research lab at Adobe Research. As a senior researcher at the principal level, you will have a broad scope to initiate, lead, and execute research initiatives in NLP, Multimodal Learning, and related areas that are relevant to Adobe’s business. You will collaborate with researchers in Computer Vision and Deep Learning on cross-disciplinary projects, and partner with product teams to transfer research outcomes to Adobe products.

In addition to pursuing individual research, a major part of your job will be leading a team of research scientists pursuing various topics in NLP and multimodal learning. As leader, you will partner closely with research and product teams to identify and prioritize problems, formulate a research strategy and roadmap, and lead the team to deliver on the roadmap. Finally, you will recruit new researchers in NLP and multimodal learning, as well as develop the existing team.

Required Qualifications
Ph.D. in Computer Science or related field
5+ years of experience in NLP, Machine Learning, or Computer Vision
Excellent publication record in NLP, Machine Learning, or Computer Vision
Excellent leadership skills
Excellent communication skills and a strong teammate

Preferred Qualifications
Experience in leading a team and hiring and developing talents
Experience in state-of-the-art NLP technologies (transformers, etc.)
Experience in multimodal machine learning (image+text, video+text, etc.)
Experience working with large-scale datasets
Track record of collaborating with researchers in adjacent domains such as Computer Vision, video, and document understanding
Experience of working with product teams on technology transfers

Data Strategy: Understands, articulates, and applies principles of the defined strategy to routine business problems that involve a single function.

Data Source Identification: Supports the understanding of the priority order of requirements and service level agreements. Helps identify the most suitable source for data that is fit for purpose. Performs initial data quality checks on extracted data.

Analytical Modeling: Selects the analytical modeling technique most suitable for the structured, complex data and develops custom analytical models.

Conducts exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data.

Defines and finalizes features based on model responses and introduces new or revised features to enhance the analysis and outcomes. Identifies the dimensions of the experiment, finalizes the design, tests hypotheses, and conducts the experiment.

Perform trend and cluster analysis on data to answer practical business problems and provide recommendations and key insights to the business. Mentors and guides junior associates on basic modeling and analytics techniques to solve complex problems.

Model Assessment & Validation: Identifies the model evaluation metrics. Applies best practice techniques for model testing and tuning to assess accuracy, fit, validity, and robustness for multi-stage models and model ensembles.

Model Deployment & Scaling: Supports efforts to ensure that analytical models and techniques used can be deployed into production. Supports evaluation of the analytical model. Supports the scalability and sustainability of analytical models.

Code Development & Testing: Writes code to develop the required solution and application features by using the recommended programming language and leveraging business, technical, and data requirements. Test the code using the recommended testing approach.

Data Visualization: Generates appropriate graphical representations of data and model outcomes. Understands customer requirements to design appropriate data representation for multiple data sets. Work with User Experience designers and User
Interface engineers as required to build front end applications. Presents to and influences the team and business audience using the appropriate frameworks and conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance, and leverages rational arguments.

Guide and mentor junior associates on story types, structures, and techniques based on context.

Problem Formulation: Translates business problems within one's discipline to data related or mathematical solutions. Identifies what methods (for example, analytics, big data analytics, automation) would provide a solution for the problem. Shares use cases and gives examples to demonstrate how the method would solve the business problem.

Applied Business Acumen: Provides recommendations to business stakeholders to solve complex business issues. Develops business cases for projects with a projected return on investment or cost savings. Translates business requirements into projects, activities, and tasks and aligns to overall business strategy. Serves as an interpreter and conduit to connect business needs with tangible solutions and results. Recommends new processes and ways of working.

Demonstrates up-to-date expertise and applies this to the development, execution, and improvement of action plans by providing expert advice and guidance to others in the application of information and best practices; supporting and aligning efforts to meet customer and business needs; and building commitment for perspectives and rationales.

Provides and supports the implementation of business solutions by building relationships and partnerships with key stakeholders; identifying business needs; determining and carrying out necessary processes and practices; monitoring progress and results; recognizing and capitalizing on improvement opportunities; and adapting to competing demands, organizational changes, and new responsibilities.

Models compliance with company policies and procedures and supports company mission, values, and standards of ethics and integrity by incorporating these into the development and implementation of business plans; using the Open Door Policy; and demonstrating and assisting others with how to apply these in executing business processes and practices.

Machine Learning Engineer and/or Data Scientist

Primarily focused on Computer Vision

Preferred Qualifications:

Expert in pytorch, tensorflow, or non-python equivalent
Very strong skills in statistics, numerical optimization, linear algebra, and image processing
Familiar with realtime video processing model development, transfer learning, domain randomization model optimization, deployment, and monitoring recent research in ML/CV (e.g. CVPR, NeurIPS, ICML).

Additional Job Description

Live our Values

Culture Champion

• Models the Walmart values to foster our culture; holds oneself and others accountable; and supports Walmart’s commitment to communities, social justice, corporate social responsibility, and sustainability; maintains and promotes the highest standards of integrity, ethics and compliance.
Servant Leadership

• Acts as an altruistic servant leader and is consistently humble, self-aware, honest, and transparent.

Embrace Change

Curiosity & Courage

• Demonstrates curiosity and a growth mindset; fosters an environment that supports learning, innovation, and intelligent risk-taking; and exhibits resilience in the face of setbacks.

Digital Transformation & Change

• Seeks and implements continuous improvements and encourages the team to leverage new digital tools and ways of working.

Deliver for the Customer

Customer Focus

• Delivers expected business results while putting the customer first and consistently applying an omni-merchant mindset and the EDLP and EDLC business models to all plans.
Strategic Thinking
• Adopts a holistic perspective that considers data, analytics, customer insights, and different parts of the business when making plans and shaping the team’s strategy.

Focus on our Associates

Diversity, Equity & Inclusion
• Identifies, attracts, and retains diverse and inclusive team members; builds a high-performing team; embraces diversity in all its forms; and actively supports diversity goal programs.
Collaboration & Influence
• Builds strong and trusting relationships with team members and business partners; works collaboratively and cross-functionally to achieve objectives; and communicates with energy and positivity to motivate, influence, and inspire commitment and action.
Talent Management
• Creates a discipline and focus around developing talent, promotes an environment allowing everyone to bring their best selves to work, empowers associates and partners to act in the best interest of the customer and company, and regularly recognizes others’ contributions and accomplishments.

A postdoctoral position is available in Harvard Ophthalmology Artificial Intelligence (AI) Lab (https://ophai.hms.harvard.edu) under the supervision of Dr. Mengyu Wang (https://ophai.hms.harvard.edu/team/dr-wang/) at Schepens Eye Research Institute of Massachusetts Eye and Ear and Harvard Medical School. The start date is flexible, with a preference for candidates capable of starting in April or May 2022. The initial appointment will be for one year with the possibility of extension. Review of applications will begin immediately and will continue until the position is filled. Salary for the postdoctoral fellow will follow the NIH guideline commensurate with years of postdoctoral research experience.

In the course of this interdisciplinary project, you will collaborate with a team of world-class scientists and clinicians with backgrounds in visual psychophysics, engineering, biostatistics, computer science, and ophthalmology. You will work on developing statistical and machine learning models to improve the diagnosis and prognosis of common eye diseases such as glaucoma, age-related macular degeneration, diabetic retinopathy. You will have access to abundant resources for education, career development and research both from the Harvard hospital campus and Harvard University campus.

The successful applicant will:

1. possess or be on track to complete a PhD or MD with background in mathematics, computational science, computer science, statistics, machine learning, deep learning, computer vision, image processing, biomedical engineering, bioinformatics, visual science and ophthalmology or a related field. Fluency in written and spoken English is essential.

2. have strong programming skills (C++, Python, R, MATLAB, etc.) and in-depth understanding of statistics and machine learning. Experience with Linux clusters is a plus.

3. have a strong and productive publication record.

4. have a strong work ethic and time management skills along with the ability to work independently and within a multidisciplinary team as required.

Your application should include:

1. curriculum vitae

2. statement of past research accomplishments, career goal and how this position will help you achieve your goals

3. Two representative publications

4. contact information for three references

The application should be sent to Mengyu Wang via email (mengyu_wang at meei.harvard.edu) with subject “Postdoctoral Application in Harvard Ophthalmology AI Lab".

Are you interested in advancing fundamental AI research, publishing your work in the most prestigious AI conferences in the world, and making your code open source? How about working closely with top-notch MIT faculty, students, and IBM scientists in a flexible and fun environment? Are you passionate about opportunities to bridge your scientific work to real-world impact?

If you answered yes to these questions, and you have a strong publication record (with first-author papers in top-tier AI conferences), then you should apply to our research scientist position at the MIT-IBM Watson AI Lab. We are a community of scientists at MIT and IBM Research focused on advancing fundamental AI research ( see https://mitibmwatsonailab.mit.edu ). We are seeking a talented and highly motivated scientist with background in computer vision (and/or other areas such as NLP and speech) to join our team. You will be responsible to conduct cutting-edge research in areas such as representation learning based on synthetic data (e.g., pre-training from noise processes, graphics simulators, and synthetic languages), learning with limited labeled data, and multimodal learning (e.g., vision+language, vision+sound). You will have access to computational resources for large-scale deep learning.

Required Technical and Professional Expertise:
– Strong track record of publications in top-tier AI conferences (CVPR, NeurIPS, ICLR, etc.)
– Solid knowledge in deep neural networks and statistical inference
– Strong programming skills
– Great problem solving skills, with a strong desire for quality and engineering excellence

Apply at:
https://careers.ibm.com/job/14736006/research-staff-member-cambridge-ma…

Test

Positions (1Ph.D. student and 3 PostDocs) in the area of Computer Vision and Deep Learning, Deepfake Generation and Detection at INRIA Sophia Antipolis, France
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Positions are ideally located in the heart of the French Riviera, inside the multi-cultural silicon valley of Europe.

To apply, please email a full application to Antitza Dantcheva (antitza.dantcheva@inria.fr), indicating [Open Positions Deepfake] in the e-mail subject line.

Having access to large amounts of well-balanced, and well-labeled visual data is arguably the most important component of any learning-based computer vision system. On the other hand, labeling and collecting privacy-preserving data is typically the most time-consuming, expensive, and error-prone step in building these systems. However, most research in computer vision has been focused on improving the speed or accuracy of neural models. The goal of this position is to develop computer vision algorithms to improve the speed, precision of labeling tools, and the automatic generation of balanced datasets (from limited data). The applications will be in the area of augmented reality and virtual reality. The candidate has to have deep knowledge of generative models (e.g., VAE, GANs).

The candidate has to have publications as first author in top vision and machine learning conference (e.g., Nips, ICML, CVPR, ECCV, or ICCV).

The Computer Vision and Intelligent Systems Laboratory (https://www.cs.ryerson.ca/~wangcs/cvis.html) at Ryerson University is looking for a Postdoctoral Fellow in the broad areas of computer vision and machine learning/deep learning. The ideal candidate should have a Ph.D. in a related area with demonstrated ability to conduct high-quality research in computer vision and machine learning, and have a track record of publications in top venues. The position is for 2 years with a possibility for renewal. The start date is flexible.

Potential candidates are required to email your CVs with contact information of at least two references to Dr. Wang at wangcs@ryerson.ca

UII America, Inc., a subsidiary company of Shanghai United Imaging Intelligence Healthcare Co. Ltd. (UII), is building an organization of highly-motivated, talented and skillful AI experts and software developers to strengthen our R&D power and address the need of our innovative products in the USA market. United Imaging Intelligence (UII) is committed to providing AI solutions for medical devices, imaging, and diagnosis – to helping clients better understand and embrace AI. United Imaging Intelligence is led by two world-renown leaders in the AI industry. Together, they will lead UII in focusing on “empowerment” and “win-win.” UII empowers doctors and equipment in order for doctors and hospitals to win, for research institutions to win, and for third-party companies to win. UII America, Inc. is building a world-class research and development team in Cambridge, MA.

We have immediate openings for Computer Vision Research Interns with the following qualification requirements:

-Ph.D./M.S student in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, Applied Mathematics, or other related fields;
-Self-motivated and demonstrated problem solving and critical thinking skills;
-Familiar with at least one mainstream deep learning toolkit, e.g., Pytorch, Tensorflow;
-Familiar with Python, C++ and OpenCV;
-Proven track record of publications in the top computer vision and machine learning venues such as CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, AAAI, TPAMI, and IJCV is a plus;
-Experience with 6D pose estimation or 3D visual perception is a plus;
-Excellent communication skills and team-work spirit.

Main Responsibilities

-Conduct top-tier research in the area of Computer Vision in a collaborative team-working environment;
-Working closely with full-time employees to come up with, implement, and verify research ideas;
-Fast prototyping, and developing cutting edge AI assets for the company;
-Contribute to intellectual properties, strong publications and transferring technologies into practical product solutions;
-Be ambitious to change future Healthcare with innovations.