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Senior Machine Learning (ML) Scientist - Signal Processing and Inverse Problems
United States, Washington, Redmond
·
On-site
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Full-time
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2mo ago
Health Futures is a Research and Incubation team working at the intersection of computer science, signal processing, machine learning, and biomedicine. We are a global and diverse team of engineers, scientists and medical doctors who are working on next-generation Artificial Intelligence (AI) tools and methods for health and life sciences. We offer a unique and vibrant environment that features innovative academic research, enterprise software development, and real-world delivery, with close feedback loops and rapid iterations among all three, much like a lean startup. Our mission is to empower every person on the planet to live a healthier future.
We are looking for a researcher to help us advance AI based techniques for processing real-world signals and measurements from medical instruments. The specific focus for this role will be on developing AI that can be used to transform and enhance signals from medical instruments. The specific focus area will be on medical imaging raw data and how the raw signals are transformed to images and ultimately to clinically relevant insights. The work is focused on inverse problems and the modelling approaches that can be used to solve them. You will lead efforts to train and test models for image reconstruction and for image restoration tasks such as denoising and super-resolution. In this role, you will leverage your background in signal processing, inverse problems, and image reconstruction and explore how the latest innovations in AI might help us improve advanced medical imaging techniques. You will need to take a hands-on approach to experiments and implementation, and you will be expected to write code that can be shared internally and externally.
#healthfutures #ai #signalprocessing #biomedicalimaging #mri #research
Responsibilities
Design and Implement New Approaches: Focus on training and tuning large-scale, multi-modal deep learning models.
Build ML Pipelines & Infrastructure: Develop robust architecture and training systems for rapid experimentation.
Research Translation: Collaborate with researchers and domain experts to convert cutting-edge research into practical solutions.
Technical Leadership: Mentor team members and advise on strategic technical directions.
Publications: Write scientific publications that demonstrate how AI might transform medical imaging and present our work at scientific meetings.
Qualifications
Required
Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
Preferred
3+ years of experience with the application of machine learning techniques to inverse problems and/or image reconstruction.
Experience with diffusion models for solving inverse problems.
Hands-on experience with distributed training of deep learning models.
Experience with applying AI to image reconstruction and restoration.
Experience working in large, shared code bases.
Proven track record for publishing in scientific journals.
#healthfutures #ai #signalprocessing #biomedicalimaging #mri #Research
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
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