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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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-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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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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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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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