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programs. To learn more about UofSC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Research Grant or Time-limited positions may be eligible for all, some, or
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information at BNL | Benefits Program JOB DESCRIPTION: We are looking for a Postdoctoral Research Associate in theoretical condensed matter physics, with interests including but not limited to: exotic phases
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and free-energy calculations in explicit solvent. The postdoctoral researcher will employ machine-learning-accelerated methods throughout the workflow, contribute to the development of new computational
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that are critical to creating effective learning and work systems within engineering. The postdoctoral scholar will collaborate with Professor Alexandra Coso Strong to examine how institutions build capacity (e.g
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), electrophysiology, genotyping, brain stimulation (tES, TMS), computational modeling and/or machine learning. For all our projects, we seek post-doctoral researchers who aim to take leading roles in projects
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partners across the aerospace industry. BladeUp aims to transform wind turbine blade design and manufacturing using advanced modelling, machine learning and high-performance simulation, while Gradient Shells
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values align with our Guiding Values and Principles , Principles of Community , and Strategic Plan . At UC Berkeley, we believe that learning is a fundamental part of working, and provide space for
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lab’s website https://sites.duke.edu/corinnelinardiclab/ . Be You Work Performed · Perform literature reviews to guide research · Design and execution of standard in vitro assays ongoing in the lab
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communication skills. • Track record of conducting original research and publishing in peer-reviewed scientific journals. Preferred Qualifications • Experience in remote sensing or machine learning applications
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mechanistic chemical biology research, the postdoctoral fellow will learn and gain experience in small molecule drug discovery from target validation, chemical probe discovery to preclinical mechanistic studies