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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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predict molecular subtypes and therapeutic responses. These tools power a precision oncology framework that enables data-driven treatment selection. In parallel, we generate patient-derived 3D
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non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar. Responsibilities
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organisms. Please note that we have a parallel search for computational neuroscientist faculty members at https://apply.interfolio.com/190740 . The successful candidates will be embedded in a richly
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-throughput experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning
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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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RF and mixed-signal systems. This role offers the chance to work at the intersection of RF integrated circuits, analog computing, and intelligent sensing systems, an area that is becoming increasingly
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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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Yale School of Medicine is a leading academic medical institution dedicated to advancing health through discovery, education, clinical care, and service. The Department of Internal Medicine and its