65 algorithm-development-"Prof"-"Prof" Postdoctoral positions at Princeton University
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develop and apply computational approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a
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, collaborative, and vibrant research environment. Princeton University is in an idyllic college town halfway between New York City and Philadelphia, with convenient train access to both cities. This opportunity is
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to apply and further develop their technical skills in a dynamic research environment. The successful candidate will have the opportunity to work closely with our engineers and other research staff. Projects
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Branch is dedicated to accelerating the study of metabolic phenomena associated with cancer to develop new paradigms for cancer prevention and treatment. Its main research areas include: - Metabolic
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Fellows Program. The Program recognizes and supports outstanding early-career scientists who can make important research contributions in the areas of ecology, evolution, and/or behavior, while also
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collaborate with the ARG team on developing grant proposals. Qualifications Required qualifications: Doctoral degree in a related field, such as Computer Science, Robotics, Civil Engineering, Architecture, etc
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fabricated structures, and assisting with managing the lab and projects. We also expect that you will collaborate with the ARG team on developing grant proposals. Qualifications Required qualifications
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processes and novel materials in several research thrusts: i) development of advanced manufacturing processes for low-cost battery cathode active materials production for lithium-ion batteries, ii
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on developing new systems models to examine social and biological drivers of infection inequality. The overarching goal of this postdoctoral position is to advance the use of mathematical and statistical models
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computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models of reinforcement learning in the brain and