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) , Lattice QCD , Machine Learning , Neutrino Astronomy , Neutrino physics , Nuclear and Many-Body Theory , Nuclear Theory (nucl-th) , Particle Astrophysics , Quantum Field Theory Appl Deadline: 2026/01/01 11
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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processes that use data-driven machine learning. Given the span of the IN-CYPHER programme, we are seeking multiple motivated research fellows. Unique in its scope, we are developing technologies that span
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that combine machine learning and classical methods. Work Plan: -State-of art revier and publication of a review paper -Development of classical approaches -Development of hybrid approaches -Journal publication
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Stig Brøndbo 22nd October 2025 Languages English English English Faculty of Science and Technology PhD Fellow in Knowledge-Driven Machine Learning Apply for this job See advertisement The position A
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of the programs is to advance foundational and applied research in contemporary artificial intelligence (AI) and machine learning (ML), as well as AI-enabled science. Specifically, our goals are to pioneer cutting
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research interests compatible with those of our current faculty in the following research groups: mathematical biology, mathematical and computational finance, numerical analysis, machine learning and data
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scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations. Key responsibilities
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pressure-build ups in potential multi-site storage licenses. The research will help to suggest best practices for machine learning integration in de-risking CO2 storage sites. We seek a candidate with a
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the world. Each of the four campuses (two in Chicago, one in London, and one in Hong Kong) reflects the architectural traditions of its environs while offering a state-of-the-art learning environment. Chicago