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algorithms, pangenome graphs, or population genetics Deep learning for sequence, EHR, or imaging data High-performance and GPU computing environments Excellent candidates from adjacent quantitative
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Technology Studies. Preferred focus areas: critical computing/artificial intelligence studies; labor and automation technologies; medicine/alternative medicine; chronic illness and/or disability studies
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Dr. Rudolf Jaenisch at MIT, where he studied stem cell reprogramming, and thereafter, in the laboratory of Dr. Richard Lifton at Yale during his MD/PhD, where he focused on the genetics of complex
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algorithms and running these models on high performance GPU computing clusters. The training will also provide knowledge about the recent advances in large language models. Mentoring: The
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understanding of machine learning algorithms in scientific computing, with emphasis on cryo-EM applications * AI for computational mathematics: Developing tools and methodology for using AI assistants in
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and high-performance or GPU-accelerated computing environments. Applying rigorous methods for external validation, transportability, subgroup performance, fairness, uncertainty quantification, and
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Qualifications High-performance or cloud computing experience. Experience with large data sets, including spatial data, administrative data, or consumer data. Benefits Yale offers an excellent and
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collaboratively in an interdisciplinary team. Good to have Experience in smart contract development, preferably in Solidity. Familiarity with cloud computing and shell script. Knowledge of biomedical
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oral and written communication skills, and the ability to work effectively with a wide range of constituencies in a complex and diverse setting Proficiency with statistical computing (e.g., R, Python or
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, 2021) in kidney cancer. The ideal candidate will have a strong background in computer/data science (including statistics), knowledge of R and python, and experience with high performance computing