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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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Applied Mathematics, Computer Science, or Theoretical Physics (at the time of appointment). Background in machine learning theory or in one or more of: high-dimensional probability, random matrix theory
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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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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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) in machine learning or a closely related field you should possess sufficient specialist knowledge in the discipline to work within established research programmes and have an ability to manage own
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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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), 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
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Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the
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position within a Research Infrastructure? No Offer Description Aalto University is looking for an Postdoctoral Researcher in Artificial Intelligence / Machine Learning Engineering [Academic Research
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 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