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are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance the development
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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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collaborative environment. Topics of interest include: Optimization for Machine Learning: Preconditioning methods for large-scale and stochastic optimization. Efficient deep learning using low rank and compressed
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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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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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/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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principals to problem solve work. ● Ability to maintain detailed records of experiments and outcomes. ● Ability to quickly learn and master computer programs, databases, and scientific applications
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and other machine learning models (especially neural network models, time-series models) and coding in python and R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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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