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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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. The successful candidate will be joining the Quantum Optics Theory group led by Prof. Dr. Maciej Lewenstein. The successful candidate will work on Machine Learning research. Share this opening! Use the following
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 3 months ago
research visits, fostering the dissemination of the findings and collaborations within the academic community. The research topic focuses on fundamental developments of a novel learning framework for
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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Post-doctoral Position in Machine Learning
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, United States of America [map ] Subject Area: Physics / Machine Learning Appl Deadline: 2025/12/01 11:59PM (posted 2025/09/10, listed until 2026/03/10) Position Description: Apply Position Description The William H
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in