Machine Learning for Signal Processing

Tufts University has an opening for a post-doctoral researcher to engage in a cross-cutting project focusing on the development of and use of Natural Language Processing (NLP) for social sciences applications. Recent advances, starting with the now classical word2vec approach to models built using attention and transformer networks such as BERT and GTP3 have shown tremendous potential for natural language modeling and automated interpretation. While most applications currently focus on content generation, user interfaces (chatbots), and understanding news content, this project aims to use NLP as a major assistive technology to gain insights into student work and understanding in STEM education systems. This presents novel challenges, to interpret, for example, whether a student is arguing from intuition or from formal principles, whether they are excited or intimidated, whether they are uncertain or confident. The data may be written work or audio-video streams of  conversations, and analysis of the latter may involve video processing of  gestures and tone of voice. This multi-modal analysis is the next frontier in NLP and will require novel advances in both statistical machine learning and deep learning architectures. This position provides a unique opportunity to develop and collaborate with exclusive data sets, in research designed to influence classroom teaching and impact.

Applicants must have a PhD in electrical engineering, computer science, applied mathematics, statistics, or a similar field; a background of research in the learning sciences would be helpful but is not required The ideal candidates will have experience with and a publication record in one or more of the following areas: modern methods of statistical signal processing, machine learning, optimization, or data science with applications to NLP. Programming experience in Matlab or python is highly desired and preferred. 

The post-doc will be jointly supervised by a team of faculty in machine learning (Prof. Eric Miller, Prof. Shuchin Aeron) and by a team of faculty in the learning sciences (Prof. Julia Gouvea, Prof. David Hammer).

For more information about this position, please email Prof. Shuchin Aeron (shuchin.aeron@tufts.edu) Prof. Eric Miller (eric.miller@tufts.edu) and Prof. Bree Aldridge  (bree.aldridge@tufts.edu). Interested candidates should provide Prof. Miller with a copy of their CV, list of references, cover letter, and copies of relevant articles, theses, technical reports etc.

Health Scientist Program Officer

The National Institute on Deafness and Other Communication Disorders (NIDCD) is recruiting a Program Director/Health Science Administrator (HSA) (GS-12/13/14) with expertise and research experience in data science and cloud computing efforts leveraging "big data" for biomedical research. Salary is commensurate with individual qualifications and professional experience. A full benefits package is available, including retirement, health insurance, life insurance, long-term care insurance, annual and sick leave, and Thrift Savings Plan (401K equivalent). We anticipate that the vacancy announcement for an HSA Program Officer will be posted later this summer at   http://jobs.nih.gov/globalrecruitment.

The successful candidate will:

Advise NIDCD-supported investigators across all research portfolios on implementing best practices from biomedical data science for data collection, storage, analysis, use, and sharing to ensure widespread access and accelerate the discovery of insights that will improve the lives of people with communication disorders. Promoting the use of existing data repositories in the cloud whenever possible.

Manage a research portfolio of data science grant awards conducted across the United States and internationally as well as identify scientific gaps and opportunities in the NIDCD's mission areas. This will require organizing workshops to engage stakeholders, promotion of cloud-based data sharing practices, and identifying future research opportunities.

Assist NIDCD staff in maintaining compliance with NIH data management and sharing policies. This will include participating in data science collaborations across NIH, outreach efforts to academic institutions, developing common data elements (CDEs), serving as a data science spokesperson, supporting internal activities to advance data science capabilities, and reviewing data sharing requirements.

Preferred Skills and Qualifications

• Expertise in cloud computing platforms, data repositories, machine learning, and other activities requiring significant data science knowledge.
• A doctoral degree in engineering or bioinformatics and experience with cloud-based computing for biomedical research.
• Expertise with the NIDCD's research areas is not required, and individuals at early- to mid-career stages are strongly encouraged to apply.

The NIDCD is deeply committed to diversity of thought, equity, and inclusion, and encourages applications from qualified women, under-represented minorities, and individuals with disabilities. HHS, NIH, and NIDCD are equal opportunity employers.

Please contact Roger L. Miller, Ph.D., program director of neural prosthesis development and program coordinator of SBIR/STTG grant programs, with questions or interest and check this website for updates: 

Health Scientist Administrator (HSA Program Officer, Data Science) | NIDCD (nih.gov)

Research Engineer (Research Fellow) in Sound Sensing

       Location: University of Surrey, Guildford, UK

       Closing Date: Monday 08 August 2022 (23:59 BST)

       Further details: https://jobs.surrey.ac.uk/025022-R

Applications are invited for a Research Engineer (Research Fellow) in Sound Sensing, to work full-time for six months on an EPSRC-funded Fellowship project "AI for Sound" (https://ai4s.surrey.ac.uk/), to start September 2022 or as soon as possible thereafter.

The aim of the project is to undertake research in computational analysis of everyday sounds, in the context of a set of real-world use cases in assisted living in the home, smart buildings, smart cities, and the creative sector. 

The postholder will be responsible for designing and building the hardware and software to be developed in the fellowship, including sound sensor systems, open-source software libraries and datasets to be released from the project.

The postholder will be based in the Centre for Vision, Speech and Signal Processing (CVSSP) and work under the direction of PI (EPSRC Fellow) Prof Mark Plumbley.

The successful applicant is expected to have a postgraduate qualification in electronic engineering, computer science or a related subject, or equivalent professional experience; experience in software and hardware development relevant to signal processing or sensor devices, and experience in software development in topics such as audio signal processing, machine learning, deep learning, and/or sensor systems.  Experience in development and deployment of hardware sensors, Internet-of-Things (IoT) devices, or audio systems; and programming experience using Python, C++, MATLAB, or other tools for signal processing, machine learning or deep learning is desirable. Direct research experience, or experience of hardware or software development while working closely with researchers, is also desirable.

CVSSP is an International Centre of Excellence for research in Audio-Visual Machine Perception, with 180 researchers, a grant portfolio of £26M (£17.5M EPSRC), and a turnover of £7M/annum. The Centre has state-of-the-art acoustic capture and analysis facilities and a Visual Media Lab with video and audio capture facilities supporting research in real-time video and audio processing and visualisation. CVSSP has a compute facility with 120 GPUs for deep learning and >1PB of high-speed secure storage.

The University is located in Guildford, a picturesque market town with excellent schools and amenities, and set in the beautiful Surrey Hills, an area of Outstanding Natural Beauty.  London is just 35 minutes away by train, while both Heathrow and Gatwick airports are readily accessible.

For more information about the post and how to apply, please visit:

       https://jobs.surrey.ac.uk/025022-R

Deadline: Monday 08 August 2022 (23:59 BST)

For informal inquiries about the position, please contact Prof Mark Plumbley (m.plumbley@surrey.ac.uk).

The Signal Acquisition, Modeling, Processing and Learning (SAMPL) lab headed by Prof. Yonina Eldar at the Weizmann Institute of Science is recruiting post-doctoral students for cutting-edge research at the intersection of signal processing, information theory and learning. The work will be performed in collaboration with Prof. Muriel Medard at MIT, working with collaborative and supportive teams. Background in one or more of the above areas required with the desire to expand into the other areas.

Positions: The Gordon Center for Medical Imaging (GCMI) in the Department of Radiology at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has multiple openings for highly qualified individuals at the postdoctoral level to work with Prof. Kuang Gong on NIH funded projects related to PET image reconstruction, medical image analysis, and machine learning methodologies.

The research projects aim to utilize machine learning-based image reconstruction and analysis to improve the diagnosis and progression tracking of Alzheimer’s disease (AD) and cancer. The projects are based on collaborations with clinicians from MGH, Harvard Aging Brain Study (HABS) and MD Anderson Cancer Center (MDACC). The successful candidate will have joint appointments at MGH and HMS.

Requirements:

·      Applicants should have earned a Ph.D. in engineering, statistics, mathematics, physics, neuroscience, or a related field.

·      Strong analytical, quantitative, programming and communication skills are essential.

·      Prior research experience with image reconstruction or medical image analysis is required.

·      Prior research experience with machine learning or deep learning, and proficiency in Pytorch/TensorFlow programming is desirable.

·      Applicants should be self-motivated and able to work independently as well as in a collaborative environment.

Environment: The Department of Radiology offers extensive core research facilities, including a new digital time-of-flight PET/CT scanner, brain and whole-body PET/MRI scanners, small animal PET/SPECT/CT systems and several MRI scanners, including two 7T ultra-high-field scanners. It also includes a large-scale computing facility for image analysis, network training, tomographic reconstruction, Monte Carlo simulation, and other computationally intensive research applications. The successful applicant can interact collaboratively with a large, growing research group in diverse areas of imaging technology and applications.

Apply: The positions are available as of June 2022 and the start date is flexible. If interested, please send your curriculum vitae, a cover letter describing your background and research interests, and contact information of three references to Prof. Gong (kgong@mgh.harvard.edu). Applications will be reviewed in a rolling basis until the positions are filled.

Job Description

An opportunity has arisen for a Sr. Scientist postion for the Data Science and Informatics group in the Pharmaceutical Sciences Organization.  The role will be essential in ensuring our organizations realization of our digital transformation, and to provide centralized support and data science expertise to teams across the Pharmacuetical Sciences organiztion of Merck Research Labs. Our projects focus on key development and application of machine learning (ML) to multiple teams: bleeding edge research and experimentation, common tool and platform development, and establishing best practices to ensure the scientific community at MRL is well-positioned to rapidly meet the challenges of modern R&D, and make an impact on an entire scientific culture at Merck.

As our project scope is vast, we incubate a number of ML and AI projects, as well as collaborating on larger projects across MRL, in many areas including advanced predictive modelling, computer vision, signal processing, probabilistic reasoning, and more. Our group excels in the ‘how’ of data science – how to write repeatable code, how to train a model continuously with uncertainty quantification, and how to package and deploy it into a production system. You will be responsible for setting the scientific strategy for many of these ‘how’s, tactically engaging with key groups of business and IT to determine the mechanics of training and deploying models, and working with our platform teams to build or adopt repeatable tools and packages.

In this role you will work with a diverse group of scientists and engineers to broadly shape and lead how we build, train, interpret, package, investigate, and deploy novel machine learning models and other data science artifacts at MRL. You will apply state of the art data science technology platforms to build and deploy operational solutions that span the scientific, executional, compliance, and operations spaces.  Your responsibilities include developing models for equipment, material, modality/formulation, and product and formulation specific analytics.  Responsibilities also including automation of analysis, data acquisition, workflow builds and deployment of hands-free methods.  To enable ML and AI, you will also lead machine vision for scene understanding and anomaly detection.  Solutions for resource optimization, scheduling, and forecasting, maintenance, alarms diagnosis, asset management, and develop models to explore what if scenarios are all in scope of this role. 

Reporting into the Pharmaceutical Operations and Clinical Supplies Organization (POCS) the successful candidate will bring energy, knowledge, innovation to carry out the following:

• Develop novel algorithms in statistical and computational ML, including deep learning, at different scales suitable and explainable for problems and data of varying size and quality (e.g., missing data).
• Understand deeply the strength and weakness of both conventional and new ML algorithms from different research communities, and then innovate the methods for rigorously formulated problems
• Lead interactions with the scientific and operations functions (e.g., spectral analysis or supply chain management) to identify, catalogue, develop and characterize ML use cases and value-generating data science opportunities for the respective business areas.
• Evaluate, prioritize, and plan the staged execution of data science projects. 
• Independently and in teams build, test, and deploy end to end data science solutions. 
• Serve as a subject matter expert on data science, ML, and statistical analysis.
• Build, develop, and grow the data science capabilities for both our data science staff and our functional area scientific staff via training, coaching, and mentoring.
• Help operationalize numerous models across the organization, provide guidance and training to DS and engineers alike
• Set the standard with IT for applied machine learning at MRL.

In order to excel in this role, you will more than likely have:

• A PhD with a minimum of 0-5 years’ experience, 12 years’ (for MSc) or 14 years’ (for BSc) in ML focused academia or industry
• Education and or related experience in statistical and computational ML (Computer Science, Electrical and Computer Engineering, Physics, Chemistry, Material Sciences, Pharmaceutical Sciences).
• Demonstrated experience with the mechanics of training and deploying ML models
• Strong and broad knowledge of ML and statistics, with the ability to deep dive as well as converse at high technical level with architects and senior scientists. Computer Vision and/or other deep learning experience a must.
• Strong coding skills (python and/or R, including pytorch or tensorflow) for good aspects of data engineering, ML, and statistical modeling and analysis
• Experience bridging the gap between domain subject and data science groups, with understanding of priority and business targets.
• Exposure with pharmaceutical formulation development, and characterization is a plus, but not a must.

As a company, we are committed to ‘Inventing for Life’ in all that we do. We keep the patient at the very heart of all that we do and strive to find solutions and treatments for some of the world’s most challenging healthcare needs.  We are proud to be a company that embraces the value of bringing diverse, talented, and committed people together.

If you like a rich set of varied responsibilities, with tremendous exposure and impact within a large organization, this is a role for you!  If you are ready to:

Invent solutions to meet unmet healthcare needs, please apply today. 

We have multiple openings for PhD students, Postdoctoral Researchers and R&D Engineers at Télécom Paris, Institut polytechnique de Paris, in the “Signal, Statistics and Learning (S2A) team. 

All positions are located at Telecom Paris, 19 place Marguerite Perey, 91120 Palaiseau, France.

Start of the positions: October/November 2022 (for PhDs/Engineer), January 2023 for PostDoc

Subject:

The positions will be a part of the ERC Advanced (2022) – HI-Audio (Hybrid and Interpretable Deep neural audio machines) project, which aims at building hybrid deep approaches combining parameter-efficient and interpretable models with modern resource-efficient deep neural architectures with applications in speech/audio scene analysis, music information retrieval and sound transformation and synthesis.

The potential topics include (and are not limited to): Deep generative models, adversarial learning, Attention-based models and curriculum learning, Statistical/deterministic audio models (signal models, sound propagation models,…), Music Information Retrieval software platform development (R&D Engineer position)....

Candidate Profile: 

- For the Phd positions: A masters degree in applied mathematics, datascience/computer science or speech/audio/music processing is required.

- For the Postdoc position: PhD degree and publications in theory or applications of machine learning, generative modelling, discrete optimal transport or signal processing, ideally with applications to Speech/Audio/Music signals.

- Master internship positions will also be open in early 2023.

Télécom Paris, and the S2A  team:

The S2A team gathers 18 permanent faculties covering a wide variety of research topics including Statistics, Probabilistic modeling, Machine learning, Data science, Audio and social signal processing. On the overall, Télécom Paris’ research counts 19 research teams and covers various domains in computer science and networks, applied mathematics, electronics, image, data, signals and economic and social sciences. Télécom Paris (https://www.telecom-paris.fr/en/home) is a member of IMT (Institut Mines-Télécom), and is a founding member of the Institut Polytechnique de Paris (IP Paris, https://www.ip-paris.fr/en), a world-class scientific and technological institution which is a partnership between five prestigious French engineering schools  with HEC as a key partner.

Application:
- There is no specific deadline. Applications are welcome until all positions are filled.

- In the application, please send a resume, a motivation letter (and full transcript grades for Phd/Engineer positions) to Gaël Richard, firstname.lastname@telecom-paris.fr. At least one reference letter will be asked in a second step.

The MIT Laboratory for Information and Decision Systems (LIDS) at the MIT Institute for Data, Systems, and Society (IDSS) and the MIT Schwarzman College of Computing is seeking applicants for a Postdoctoral Scholar to perform independent research in the broad areas of machine learning and intelligent systems, mentored by Prof. Navid Azizan (azizan.mit.edu). We are looking for candidates with proven excellence in research who have the vision and interest to contribute to interdisciplinary research on foundations of deep learning, optimization, dynamical systems, and control, with applications to robotics, autonomous systems, smart grids, and societal networks. The position is available immediately with a start date of September 1, 2022, or earlier.

Candidates who are currently interviewing for faculty positions but would like to spend a year at MIT before starting their faculty careers are especially encouraged to apply.

Job Requirements: Doctoral degree (expected or obtained) in engineering, computer science, operations research, mathematics, or a related field; strong analytical and written communication skills; and ability to work effectively in an interdisciplinary environment.

Candidates must submit the following documents in PDF format to AcademicJobsOnline at https://academicjobsonline.org/ajo/jobs/21522: CV/resume, a research statement, 3 representative publications, and the names and addresses of 2 or more individuals who will provide letters of recommendation.

This will be a one-year appointment, with the possibility of renewal for a second year based upon satisfactory performance.

MIT is an Equal Opportunity/Affirmative Action employer.

Machine learning and specifically deep learning techniques are promising tools in medical image analysis and they have demonstrated very good performances in many tasks, such as image segmentation. These techniques are though data demanding and as such they need large-scale cohorts, often multi-centric datasets. In this context, Domain Adaptation (DA) has recently raised strong interests in the medical imaging community as the generalization of algorithms to unseen data (domain shift), different input data domains (missing modalities) and the uncertainty of the networks output due to domain shift are still open problems. Nevertheless, all these aspects are crucial in order to translate AI models for medical image analysis methods to be evaluated in large-scale heterogenous imaging acquired in clinical practice.

In this context, we are looking for a full-time post-doctoral researcher to join the CIBM Signal Processing CHUV-UNIL section. The researcher will focus on domain adaptation, federated learning and other generalization solutions for AI-based reconstruction, segmentation and classification for Magnetic Resonance Imaging (MRI) analysis. This position aims also to investigate different aspects of explainable AI linked to domain shifts. The research will be conducted in the context of AI segmentation and classification models for assessment of advanced imaging biomarkers in Multiple Sclerosis.

Your profile

• A PhD degree in engineering, electrical engineering, computer science, physics or related fields

• Strong background in image processing and deep learning techniques is a must, with published papers in key journals (TMI, MedIA, etc) and conferences in the field (MICCAI, MIDL, NEURIPS, CVPR, etc).

• Demonstrated previous experience in different aspects of domain adaptation in reconstruction/segmentation or classification problems is required.

• Experience in neuroimage analysis is a plus.

• You certify proficiency in programming (Python, PyTorch/Keras, Javascript, bash, etc)

• You are eager to supervise and transfer your knowledge to master and PhD students and promoting a collaborative environment within CIBM sections.

• You have excellent written and oral communication skills in English; French is a plus.

• Rigorous work habits, a curious and critical mind, and a good sense of initiative.

• A high-level perseverance and a strong personal commitment are expected.

How to apply

Please send your CV, two references and a motivation letter to Dr. Meritxell Bach Cuadra (meritxell.bachcuadra@unil.ch).

Open call can be downloaded here.