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Field
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-atomic potentials using a combination of classical and machine-learning (ML) approaches (and a new hybrid method recently developed in our group). Some of the types of simulations that will be performed
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splitting and C–N coupling reactions. Work includes computational modeling of carbon-based materials, conducting simulations to understand reaction mechanisms, and developing and applying machine learning
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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and standing recognised by the University/profession as appropriate for the relevant discipline area (e.g., AI/Machine Learning, Bioinformatics). A proven track record of research and scholarly
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one or more of the following areas: complex quantum processes, quantum error corrections, tensor networks, optimisation and machine learning, and developing software infrastructure Some experience in
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You Completion (level A and B) or near completion (level A) of a PhD in the field of Information Retrieval, Natural Language Processing, or Machine Learning on Textual Data. Demonstrated expert