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attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O. Conte Center on the "Cognitive Thalamus". The successful
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on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution earth system model data, with an emphasis on Seamless
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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
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, such as survey and sampling design and data analysis (in R or Python), meta-analysis and/or document/text analysis, or computational modeling *An interest in mixed-methods approaches, including also
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health services to Princeton University faculty, staff, and employees. An integrated, evidence-informed model guides all UHS practices and services. UHS leverages clinical encounters and prevention efforts
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information about the lab, please visit https://mesa-lab.org/. Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging to investigate: 1) How immune cells localize
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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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C.V. online. Upon request, candidates should be prepared to submit references, code and/or writing samples, and transcripts. The final candidate will be required to complete a background check. The work
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specific experience in Large Language Models (LLMs), and Vision-Language Models (VLMs) Excellent programming skills (Python is required, C# and C++ is desired) Fluency in English Desired qualifications
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of interest include: Metabolomics, isotope tracing, metabolic flux analysis, quantitative modeling, mass spectrometry imaging, cancer metabolism, small molecule inhibitor discovery, dietary impact on cancer