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learning Python libraries, such as NumPy, pandas, scikit-learn, PyTorch, TensorFlow, or similar. Experience writing modular, maintainable, and documented code. Familiarity with Git and collaborative
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—including foundation models, multimodal representation learning, agentic workflows, evaluation frameworks, and scalable research software—that can accelerate discovery across multiple projects. A longer
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representations to mechanisms of human disease. Your work will sit at the intersection of machine learning, computational biology, and human genetics, with opportunities to lead methodological innovation
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through which genetic variation influences disease. Your work will sit at the intersection of statistical genetics, machine learning, and mechanistic biology, with opportunities to lead methodological
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diverse methodologies in a highly collaborative environment. Grow your skills You will enhance your scientific skills and professional skills in a research environment that spans multiple disciplines
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learning opportunities. What you'll bring Essential Master's degree, preferably in health sciences (clinical, biomedical, or population health/epidemiology) or related fields, or relevant experience is