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, biostatistics, data science, and related fields. Experience with human genomic data analysis. Professional programming skills, experience with high-performance computing, and cloud computing
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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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a related discipline. Strong programming skills in Python, R, or similar languages. Experience working with large biological datasets. Familiarity with Linux-based computing environments and high
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contribute to analyses and processing of data. They will become familiar with high performance computing environments and parallel processing, as well as cloud computing infrastructure. They will
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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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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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expect any candidate to cover all of them. * Clustering and expectation-maximization (EM): Understanding when k-means and EM succeed or fail in high-dimensional, noisy settings, and developing new
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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
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is desired: statistical and theoretical population genetics, high performance computing environments, statistical genetics. The ideal candidate will also demonstrate: A high level of drive and