235 formal-verification-computer-science Postdoctoral research jobs at Princeton University
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, lipid vesicles, polymer physics, active materials, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https
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at Princeton University.We welcome applications from all areas in mechanical and aerospace engineering, including but not limited to the fields of: Bioengineering Combustion and Energy Science Computational
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: 280092641 Position: Postdoctoral Researcher in dual-frequency comb spectroscopy at the nanoscale Description: The Princeton University Department of Chemistry seeks to hire a postdoctoral researcher for an
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for peer reviewed publications Qualifications*Ph.D. in Environmental/Civil Engineering, Computer Science/Engineering, Data Science, or a closely related field*Proficiency in Python or other tools and ML
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quantitative and computational social science, addressing a diverse array of new data and analytic challenges, facilitating impactful multidisciplinary collaboration, scholarly advancement, and the creation
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, working under the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025
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are of relevance. Outstanding scholars anywhere in the world are eligible to apply. NCGG invites candidates with a background in political science, economics, modern history, sociology, anthropology, law, business
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enduring impact in the areas of science, environmental conservation and patient care. Visit Moore.org or follow @MooreFound. The foundation's $185-million EPiQS initiative promotes discovery-driven research
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the guidance of Dr. Arash Adel, Assistant Professor in the School of Architecture and Associated Faculty of the Department of Computer Science. The desired start date is Spring 2025. Appointments are for one
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials