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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk
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Qualifications: PhD in a relevant field is required; relevant fields include but are not limited to epidemiology, statistics, biostatistics, machine learning, data science, or other quantitative fields. Required
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
, including molecular dynamics, quantum mechanical simulations, and machine learning. Proficiency in programming languages and computational software’s. Strong motivation and passion for research in the field
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analysis, GIS, and large environmental datasets Experience developing predictive or machine learning models for environmental systems Demonstrated record of peer-reviewed publications Experience
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of academic success for a Post-Doctoral Associate position. The research will focus on the development and use of machine learning and advanced molecular dynamics techniques for molecular simulations and to
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. Interest and experience with training/fine-tuning machine learning models would also be appreciated. Interest and knowledge of economic theoretical modelling would be a plus. Strong coding skills and
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quality and evidence grounding Calibration, uncertainty, and appropriate deferral Trace auditability and clinician-in-the-loop evaluation Profile Must Have PhD in Computer Science, Machine Learning, Medical
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://postdoc.wustl.edu/prospective-postdocs-2/ . Lab website: https://cruchagalab.wustl.edu/ . Research Projects: Plasma, CSF and Brain Proteomic analysis. Biomarker identification through the use of machine learning
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within the Department of Electrical and Information Technology works broadly with research within Cryptography, Computer Security, Wireless and Fixed Networks. The security group has around 20 members
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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk