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measurements of indoor environmental quality (IEQ), energy simulations, and machine learning modelling; (b) strong analytical and problem-solving skills, with the ability to conduct research independently
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The appointee will assist the project leader in the research project - “MLFF-agent: autonomous discovery of machine-learning force field with large language model”. He/She will be required to: (a) be the
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. The successful candidate will be able to teach one of the undergraduate courses related to AI/ML, programming languages, data structures and algorithms, operating systems, network security, visualization, and
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have expertise in applied optimization and optimal control, engineering computation, operational research, management science and applied statistics, FinTech, data science and machine learning
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conversions Optoelectronic devices, solar cells and novel optical materials Renewable energy solutions, novel batteries, carbon neutrality approaches Computational chemistry, theoretical chemistry, machine
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network and customer engagement with machine learning”. Qualifications Applicants should have: (a) an honours degree or an equivalent qualification, preferably a master’s degree or above; and (b
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health monitoring, preferably with a publication record in top-tier journals; and (c) be proficient in mainstream research frameworks for deep learning and computer vision. Applicants are invited
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econometrics, high-dimensional statistics, machine learning, nonparametric statistics, portfolio theory and stochastic processes. Our PhD program comprises a 2-year MPhil Phase with courses aimed at building a
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and computational physics High-energy physics/astrophysics/cosmology Statistical mechanics/complex systems/non-linear physics Machine learning/first-principles calculations/large-scale simulations
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given to those with research experience in drone delivery optimization, machine learning, reinforcement learning and operation research. Applicants are invited to contact Prof. Siyuan Feng at telephone