45 molecular-modeling-or-molecular-dynamic-simulation Postdoctoral positions at Pennsylvania State University in United States
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methods, stochastic control processes, dynamic programming, deep reinforcement learning. Strong track record in scientific contributions supported by peer-reviewed publications. Strong programming skills
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highly productive, dynamic research group consisting of faculty, post-docs, graduate students, and undergraduates. There will also be opportunities to collaborate with an interdisciplinary group
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policymakers. This exciting opportunity would support an embedded policy fellowship placement to train a Postdoctoral Scholar in the implementation and evaluation of research translation models, such as the
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expected to communicate across teams to integrate project-specific data into modeling. The post-doc will lead projects, collaborate with graduate students and researchers from inside and outside Penn State
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must be ready to conduct research involving the design of novel behavioral interventions, conduct human studies, and build computational models to implement existing behavioral paradigms such as Just In
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dynamic team at CCOR that currently consists of center director Dr. Savage Williams, 2 research faculty members, 2 graduate students, 5 full-time research staff, 3 nutrition educators, an administrative
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of funding. POSITION SPECIFICS Dr. Meg Bruening is seeking a highly motivated and collaborative Postdoctoral Scholar to join her dynamic research team in the Department of Nutritional Sciences within
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in statistical network modeling, with applications in health and social science data. The scholar will have an opportunity to collaborate with other researchers, and mentor graduate and undergraduate
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experimental, modeling, and analytical characterization and analysis tasks to evaluate the utility of various materials exposed to extreme environments. The successful candidate will provide materials synthesis
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) develop and apply statistical genomic methods to analyze multi-omics datasets for understanding complex disease etiology and (2) develop and apply novel statistical models to analyze EHR data