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with BIG DATA (eg. NGS data in multi-TB scale). Experience using genome alignment software (bowtie2, bwa, tophat, etc.) is desired. Fluent in one programming language (Python, C, C++ or Java) and
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disease progression Quantitative or computational skills are highly valued (e.g., Python/R, image analysis, genomics) Additional Qualifications If visa sponsorship is needed, Harvard retains the discretion
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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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systems or autonomous AI frameworks · Solid foundation in computational biology · Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX) · Strong analytical, problem solving, and communication
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Python and experience with GPU cluster environments (e.g., SLURM) are a plus. Special Instructions Please provide a CV, a Research Statement, and two or more letters of recommendation. The target start
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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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programming, such as R and/or Python is required. Able to work independently, meet specific goals and milestones, but also serve as part of a collaborative interdisciplinary team. Additional Qualifications
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developing and teaching spatial methods, especially desktop GIS, R, and Python strongly preferred. Desirable Qualifications: Interest/experience in spatial AI approaches. Comfort with research project
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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