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Health Futures is a mission-focused organization within Microsoft Research, working at the forefront of artificial intelligence, healthcare, and life sciences research. We are a global and diverse team of engineers, scientists and domain experts who are working on expanding the technological frontier of health and life sciences through deep research. 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 between all three. Our mission is to empower every person on the planet to live a healthier future. We are looking for a Senior Researcher to help us advance the ways artificial intelligence can accelerate and advance discovery in biomedicine and the life sciences. This role is ideal for a candidate with intellectual curiosity who wants to craft a research agenda, articulate it clearly to team members with a diverse set of backgrounds, and execute it as a member of that research team. Successful applicants will draw from an interdisciplinary background covering everything from deep learning fundamentals to molecular biology and will be passionate about making new discoveries in health and the life sciences.
Job Responsibility:
Design, implement, and evaluate novel methodology for scientific discovery through artificial intelligence, non-exhaustively including techniques around post-training, inference-time optimization, interpretability, and experimental design
Conduct rigorous investigations into the capabilities of deep learning models in the realm of health and life sciences research
Participate in reproducible and collaborative team-based technical research
Communicate research findings internally and externally
Requirements:
Doctorate in relevant field OR Master's Degree in relevant field AND 3+ years related research experience OR Bachelor's Degree in relevant field AND 4+ years related research experience OR equivalent experience
Expertise in machine learning for scientific discovery, including any of the following: Interpretability (post-hoc, ante-hoc, mechanistic), Reasoning and inference-time optimization, Experimental design, Other uses of AI for scientific discovery
Expertise in multimodal and large-scale deep learning in computational biology and medicine, including model design, training (such as post-training, including reinforcement learning), and/or evaluation
Experience developing software, systems, or workflows that leverage generative AI-based systems to solve real-world problems in the life sciences
Experience working with biological data such as transcriptomics, microscopy, and other -omics modalities
Experience creating robust, reproducible technical artifacts as part of an interdisciplinary team
A track record of scientific publication in any of the above domains