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learning, or related quantitative field. • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in
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research experience in computational biology, mathematical modeling, or immunology. Familiarity with numerical methods, parameter estimation, and data visualization. Some knowledge of probability and
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deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in interpretable ML and mechanistic model discovery. Submit a
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; (ii) an impact evaluation of a large-scale tree-growing program in Kenya, Tanzania, Uganda, and India; and (iii) an analysis of financial incentives for smallholder tree growing in Ethiopia. In
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inhalation exposures. We use in vitro, rodent, and non-rodent models to achieve our study objectives. We will employ various techniques/methods such as ELISA, multiplex MSD panel assays, flow cytometry, bulk
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conventional methods like nonlinear FEM, and comparing the results to computational observations. 3) Support the educational activities of the Pl through graduate student mentoring, selected lectures, and
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quantitative methods and excited about discovering physical principles of biological organization. Minimum Requirements: PhD in a scientific disciplines, ideally Biology, Bioengineering, Physics or Math
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. The appointment is not part of a clinical training program, unless research training under the supervision of a senior mentor is the primary purpose of the appointment. The Postdoctoral Appointee functions under
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backgrounds and lead annual fieldwork activities for sample collection. Opportunities to travel to conferences and project meetings are also available. This position provides numerous opportunities
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with various methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning and applying these techniques to problems in computational creativity