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to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
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Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs. The candidate will apply their expertise to advance predictive
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NSF-Simons Foundation-funded National AI Institute for the Sky | Chicago, Illinois | United States | 2 days ago
, United States of America [map ] Subject Areas: Computer Science and Electrical and Computer Engineering Astronomy / Astronomy and Astrophysics Artificial Intelligence Artificial Intelligence, Machine Learning and Autonomy
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Postdoctoral Research Scholar in Machine Learning and Computational Genomics Department of Epidemiology, School of Public Health, University of Pittsburgh The Department of Epidemiology
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: Operator Algebras, Machine Learning, Analytic Number Theory, Automorphic Forms and Representation Theory Appl Deadline: 2025/10/10 11:59PM (posted 2025/09/10, listed until 2025/10/10) Position Description
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Python and GEE for geospatial analysis and data science. Experience with machine learning/deep learning frameworks (e.g., PyTorch, TensorFlow) applied to image or geospatial data. A track record of first
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration
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to this classification. It is not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified. Summary: The Goodwill Computing Lab seeks a Postdoctoral
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of Python programming and a deep learning framework, preferably PyTorch. Solid knowledge of image processing, inverse problems, and machine learning. Significant research experience, demonstrated by quality
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, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and enrich the knowledge base (i.e. learning by