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will work with Prof. Daniel Eisenstein and collaborators on the analysis and interpretation of JWST data, with particular emphasis on deep-field observations. The position provides access to large, high
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particular emphasis on deep-field observations. The position provides access to large, high-impact JWST datasets and opportunities to contribute to multiple ongoing programs, as well as to a broader portfolio
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Science, Computer Science, Applied Mathematics, Engineering and Physics. Additional Qualifications Expertise (or desire to work) in reduced order modeling, Causal inference and High Performance Computing
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and Applied Sciences Department/Area Electrical Engineering/Computer Engineering/Computer Science Position Description Project Deep learning plays an essential role in the operation of an autonomous
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senior scientist, the RA I will design experiments, perform a variety of molecular biology techniques including molecular cloning, DNA/RNA extraction, DNA/protein gel electrophoresis, PCR, mammalian cell
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic
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Director of LISH (Dr. Ramona Pop). The position involves conducting rigorous empirical research using field experiments, large-scale data analysis, and computational methods to advance our understanding
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high-dimensional statistics, machine learning theory, or more broadly, mathematical foundations of AI. The appointment will be for up to two years with annual renewal based on satisfactory performance
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] Subject Areas: high-dimenstional statistics, Machine Learning theory, Mathematical foundations of AI Appl Deadline: none (posted 2026/03/06 05:00 AM UnitedKingdomTime) Position Description: Apply Position