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mathematical background in reinforcement learning and/or control (e.g., optimal control, decentralized control, and/or adaptive control) with a strong desire to make an impact on energy/power grids are preferred
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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of medicinal chemistry. The candidate will be responsible for designing, synthesizing, and optimizing molecules targeting GPCRs (cannabinoid receptors; allosteric and orthosteric) and ion channels (a4b2 nAChR
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, New York 14850, United States of America [map ] Subject Areas: Data Science / Statistics , Applied Mathematics , Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning
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Details Title Postdoctoral Fellow in Riemannian Optimization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description A postdoctoral position is
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the Pereira laboratory: https://www.lsi.umich.edu/science/our-labs/filipa-pereira-lab Responsibilities* Experimental responsibilities will include: Perform genome engineering of Streptomyces strains
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learning, mathematical analysis, mathematics of data, modeling & simulation, multiscale methods, numerical analysis, optimization, ordinary and partial differential equations, numerical solvers, quantum
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, optimizing, and advancing the understanding of chemical and acute non-kinetic threats, as well as medical countermeasures for safe and effective prophylaxis or treatment against these diverse challenges
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dedicated to discovering, evaluating, optimizing, and advancing the understanding of chemical and acute non-kinetic threats, as well as medical countermeasures for safe and effective prophylaxis or treatment