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– Adaptive & Agentic AI. The PhD project focuses on developing robust and reliable machine learning systems that can adapt at test time under real-world distribution shifts. Modern foundation models (e.g
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Location: Central Cambridge We are seeking a highly motivated Research Assistant/Associate in Machine Learning to join an interdisciplinary project at the University of Cambridge focused on machine
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SD-25045 – PHD IN HONG-OU-MANDEL INTERFERENCE AND ENTANGLEMENT WITH COLOUR CENTRES IN SILICON CAR...
accordance with applicable laws and institutional policies. PhD additional conditions: · Supervisor at LIST: Dr. Florian Kaiser · Work location: Luxembourg Institute of Science and Technology
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this goal, doped-diamond systems will be considered. The thermal stability of selected compounds under operating conditions will be assessed by means of molecular dynamics simulations with Machine Learning
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, including Machine Learning & Artificial Intelligence, Colour & Imaging, Computer Vision, Graphics, Data Science, Health Computing, Computational Biology, Cyber Intelligence and Networks. We collaborate with
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motivated candidate with a strong background in statistics and/or machine learning. Areas of particular interest include, but are not limited to: Causal Discovery and Causal Inference Extreme Value Theory
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description Integreat - the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work at the
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Description Your Responsibilities We are looking for a highly motivated PhD student in the areas of Probabilistic Machine Learning and Neuro-Symbolic AI to contribute to the Cluster of Excellence “Bilateral AI
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to learn innovative approaches in data analysis, including the programming, AI – tools, machine learning Mobility condition: - the candidate MUST NOT have their main activity (residence/work/studies) in
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ELLIIT collaboration, in which BTH leads computational and applied AI development. Research focus The PhD project will focus on the development of scalable and efficient machine learning approaches