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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills and familiarity with machine learning
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collaborate with other technical groups working on the design. The successful candidate will also have opportunity to conduct experiments and machine development activities on the existing accelerators. The key
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transduction, or protein-protein interaction. Computer skills in data and statistical analyses are required. A proven record of productivity as demonstrated by recent first author publications in leading peer
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in areas related to quantum information theory, quantum error correction, and machine learning. The position will start in fall 2026 and will be for two years, with a possible 1-year extension pending
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. Responsibilities: • Design and conduct experimental research on paper-based materials for food packaging applications. • Develop predictive models for packaging performance using machine learning and physicochemical
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have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large volumes of data and high-performance computing is
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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), working closely with Professor Catrin Tudur Smith and Dr Ahmed Waqas. The Postdoctoral Researcher will work on a network meta-analysis of psychosocial interventions for military personnel with Posttraumatic
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-principles and atomistic simulations with machine-learned interatomic potentials to: Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps. Construct atomistic