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and deep learning methods for large-scale genomic, clinical, and imaging biobank data, with stable multi-year NIH support. The Zhi Laboratory has a sustained track record of methods development
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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, comparative genomics, machine learning, and evolutionary analysis to address fundamental questions in molecular biology and human disease. Responsibilities Develop computational pipelines for the discovery
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collaborative work, as evidenced by publications or preprints. Enthusiasm for learning neuroimmune biology is essential; prior formal immunology training is not required. Mentoring, training, and collaborations
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include: Biomedical sensing and physiological monitoring Edge intelligence and energy-efficient machine learning hardware Radar and wireless signal processing and communications The successful candidate
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epidemiologic and statistical methods, including causal inference approaches, machine learning techniques, and high-dimensional data integration methods. Prepare first-authored manuscripts, abstracts, progress
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external collaborators of the Bakshi lab. This position will thus prepare the candidate for an independent research career. Training: The post-doctoral associate will learn skills in research study design
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. This project aims to deepen our knowledge about institutional alternatives and to share what we learn broadly. Qualifications The position is open to those who have recently completed their Ph.D. (in the last 6
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Experience writing manuscripts published in peer-reviewed journals Ability to work both independently and as part of a team Eagerness to learn and establish new methods, and good problem-solving skills
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causal inference, machine learning, and artificial intelligence is desirable ● Experience with clinical, EHR, or biobank data analyses is desirable Application Instructions To apply: Interested