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Duration: 1-year appointment with possibility of renewal for a 2nd year. About the Department Our research group works at the intersection of machine learning, medical imaging, biomedical engineering, and
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multimodal data analysis is preferred; experience with R, machine learning, or advanced statistics is a plus. Training and Research Environment: The successful candidate will receive interdisciplinary training
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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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these skills through training. Strong organizational skills and attention to detail, with the ability to manage multiple responsibilities in a learning environment. Strong interpersonal, communication