11 algorithm-development-"St"-"St" Postdoctoral positions at Pennsylvania State University in United States
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vulnerable to developing substance use disorders, when, how, why, and what might be done about it. Here, we will use the aversive behavioral and neurochemical response to the intraoral (IO) delivery of a drug
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computers or atomic-physics platforms, and quantum algorithms for quantum many-body physics. A PhD in Physics is required. The ideal candidate will have numerical simulation skills with exact diagonalization
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care and diagnostic test implementation. This post-doctoral fellowship is intended to train and prepare the applicant to compete for CPEP (ASM) accredited two-year fellowships and to pursue board
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, measurement development, and healthy aging. This position will provide training in clinical trials research and population health, and allow for the development of manuscript writing and data analysis skills
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designed to maximize the professional development of its participants and provides a research stipend. Initial offers will be made in January 2026. Applicants must complete the Penn State application and
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with sex and gender. The post-doctoral scholar will be expected to help support management of ongoing projects and develop their skills through independent research and mentorship. They will plan
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are focused on Systems Biology, Epigenetic Gene Regulation, Single-Cell Genomics and Cancer Biology. Responsibilities for this position include data analysis, method development, scholarly activities and other
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the Electrical Engineering department, and Daning Huang in the Aerospace Engineering department in the area of Scientific Machine Learning. The project is to develop computationally efficient reduced-order
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. Perform routine duties using established procedures with coordination and effective communication with the PI. Teamwork & Leadership - Independently develop and implement laboratory policies, update
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in the group of Dr. Bharath Sriperumbudur. Potential research projects include (but are not limited to) developing theory and methods for metric-valued (including functions, distributions) data