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Field
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of Energy (DOE) experimental facilities. This role involves research and development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high
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expression and purification pipelines to yield high-quality, stable viral and host targets. Optimizing and evaluating biochemical small-molecule library screening assays using purified protein targets
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Engineering Department at The Pennsylvania State University. This position involves the development and application of numerical analysis approaches using Machine Learning based multi-physics tools
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alongside guidance from senior research personnel. Key Responsibilities • Experimental Design: Develop and optimize behavioral and physiological paradigms targeting brain networks. • Data Collection: Conduct
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Suite. • Experience with numerical optimization techniques • Strong written and oral communication skills, with the ability to effectively present and publish research findings Pay and Benefits Pay Range
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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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. Duties/responsibilities Performing high level theoretical, and numerical research and data analysis (55%) Presentation publication, and proposal preparation (20%) Mentoring PhD and MSME students (10
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) methods for modeling and optimization of metallic materials and advanced manufacturing processes. Participate in the design of integrated, scalable numerical methods and uncertainty quantification. Follow
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
optimization. Participate in collaborative research projects and mentoring. Contribute to academic publications, reports and seminars The selected candidate will be part of the Foundations Cluster of CIDSAI and
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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific