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will involve training deep learning models to compress raw data into structured feature spaces required for downstream surrogate modeling. Qualifications Education and Experience: Undergraduate student
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required to pass a background check. This is a one year term position with possibility for extension. PREFERRED Previous experience with animal model systems and/or mammalian tissue culture is preferred but
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: 281522549 Postdoctoral Associate: Integrating political economy insights into energy modeling D-26-SPI-00009 | Research | Princeton School of Public and International Affairs The Peng group in the School
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. Proficiency with Python and Java Experience with Docker Strong understanding of data design, architecture, relational databases, and data modeling. Princeton University is an Equal Opportunity Employer and all
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thrusts: (a) Better design, evaluation, safety, and understanding of large AI models (especially language models) and (b) Studying the impact of large AI models on society and the world. The Initiative will
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emulator models to correct their representation of complex regimes. Education and Experience: Undergrad student. Knowledge Skills and Abilities: This project is ideal for a student interested in Scientific
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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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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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tools in collaboration with fusion industry partners. The Principal Research Scientist (Managing) is expected to exploit the expertise and state-of-the-art models developed by the PPPL Theory Department
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collected from these studies, subject recruitment, and some administrative work. Depending on qualifications/interest, the research specialist may also assist with developing computational models of learning