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should feel confident in their ability to learn about and use new mathematical and machine learning tools as needed, even if these have not formed part of their prior studies. The candidate is expected
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objectives: 1 – Development of a tool for identifying operating regimes using machine learning techniques. 2 – Development of a tool for identifying the causes of process eco-efficiency degradation using
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into open-source software tools and data portals of the institute • Collaborate with internal and external, as well as national and international, project partners Present your results at national and
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leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https://www.ln.edu.hk/ . Applications are now invited for
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Supervisor: Professor Fernanda Duarte Start date: 1st October 2026 Applications are invited for a fully-funded DPhil studentship in Machine Learning Interatomic Potentials for Metal-Ligand
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the fundamental engineering understanding of gas centrifuge systems. The group leverages analytical techniques and advanced computational tools—including finite element analysis (FEA)—to evaluate and predict
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modeling and model–data fusion techniques, and developing faster, machine-learning–based tools that can stand in for slow model simulations. These tools will be used to test how model parameters influence
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the racial justice implications of technology and algorithmic decision-making tools in the criminal legal system and other systems that govern people’s lives; challenging the forces that drive racial
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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(spoken and written), certified if not a native language; e) Basic computer knowledge from the user's perspective; f) Show professional rigor and strong ability to work in a team; g) Demonstrate resilience