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it; as well as have theoretical skills including algorithm implementation/development and data visualization. Experience and interests include designing machine learning pipelines, building web
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nano-mechanics, and machine learning as it applies to the field of computational mechanics. Candidates will be given opportunities to develop their teaching experience by designing and teaching a class
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and machine learning based software to assist clinical workflow and pre-clinical studies. Recent software developed from the group has been adopted in the clinic and preclinic labs. The scientific
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transcriptomics analysis • Interest in cancer biology and immunology principles • Excellent written and verbal communication skills Preferred Qualifications: • Experience with machine learning approaches
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/machine learning, and/or expertise in microbiome/metabolome studies. The breath of the lab’s interests are wide. We have teams focused on identifying novel diagnostics/biomarkers using human samples. In
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decision making systems, in particular the use of different optimization, machine learning, and decision making modeling techniques for problem solving. Desire to grow collaborative research and mentorship
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neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred. Demonstrated experience with large
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survival data using longitudinal features, and (6) machine learning and deep learning for analyzing time-to-event outcomes, or (7) radiomics and medical imaging analysis. Required Qualifications: We seek
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our team. We are looking for postdoc candidates to develop and apply cutting-edge technologies in spatial transcriptomics, single-cell sequencing, machine learning, and functional genomics
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a related field. Strong background in machine learning, deep learning, and natural language processing (NLP), with a focus on large language models. Proficiency in Python and machine learning