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students on foundational machine learning and domain-informed scientific applications. These positions are especially well suited to candidates prepared to contribute to advanced research programs in modern
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, spatiotemporal modeling, high-dimensional statistics. ● Proficiency in statistical programming (R and/or Python) and good practices for reproducible research. ● Experience working with large datasets and cloud
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, representation learning) Spatiotemporal modeling or geospatial/temporal data analysis Medium-to-Large-scale foundation models pretraining/fine-tuning paradigms Strong programming skills in Python and experience
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modeling or geospatial/temporal data analysis Causal inference ● Strong programming skills in Python and experience with PyTorch, required to have experience developing code with a team through collaborative
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, strong research background in statistics and ML, programming, data analysis, strong genetic and genomic knowledge, as well as good written and oral communication skills. Additional Qualifications Special
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. Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models. Strong programming skills in Python and experience building and maintaining research code. Demonstrated
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contributions. Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models. Strong programming skills in Python and experience building and maintaining research code