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a pioneer, harnessing state-of-the-art Omics technologies to dissect fundamental biological mechanisms, power large-scale supervised and foundational machine learning initiatives, and comprehensively
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developing deep-learning systems; graph neural networks & representation learning (VAEs/transformers) preferred. Experience in statistical modeling & ML for high-dimensional data (training, evaluation
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-Coding Genome Edits: Develop innovative machine learning approaches for designing precise non-coding genome edits, focusing on how non-coding alterations influence gene regulation and cellular function
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structural biology to tackle challenging scientific questions. Your responsibilities will include, but are not limited to: Multi-omics analysis of bulk and single-cell sequencing data. Developing deep learning
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discovery. This position in particular focuses on sequence-to-function deep genomics modeling, with the goal of developing performant models that make generalizable out-of-distribution predictions