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Field
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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on behalf of Stony Brook University. BSA salutes our veterans and active military members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL
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. Familiarity with machine learning (ML) techniques for optimizing models or analyzing complex datasets. Experience with space-based instrumentation or similar high-precision systems. Demonstrated expertise in
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant neuroscience community, and there are opportunities
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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healthcare. Qualifications Required: PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field. Strong background in machine learning
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/Experience PhD Deadline to Apply 12/31/2025 Our University Community We value the well-being of each of our employees and are dedicated to creating a healthy place for everyone to work, learn and live. In
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students. Qualifications: Required: PhD in Computer Science, AI, Data Science, Statistics, or related. Strong skills in machine learning and deep learning, with a fundamental understanding of LLMs