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of machine learning and advanced molecular dynamics techniques for molecular simulations and to study Nucleic acids structures and their interactions. For more information, please visit https://nyuad.nyu.edu
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characterization (PXRD and Rietveld refinement, SEM, XPS, etc) Experience with machine learning techniques, particularly machine-learned interatomic potentials, in the context of chemical research. Programming
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this, the postdoctoral researcher will combine machine learning, molecular dynamics simulations and high performance computing (Isambard AI). Applicants must have a PhD in an appropriate area of computational chemistry or
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testing of deuterium permeation experiments in support of the UKAEA’s LIBRTI project (https://ccfe.ukaea.uk/programmes/fusion-futures/librti/). This will include expanding capabilities on UoM’s Gas-Driven
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dissemination is expected to focus on leading Human-Computer Interaction venues. For further information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders
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top-tier venues and will have dedicated time for advancing their own research. The project is in collaboration with CCL (The Center for Connected Learning and Computer-based Modeling, PI Uri Wilensky
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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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mixing. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research
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that prioritise student learning in the age of AI. Job requirements You hold a PhD in Educational Science, Learning Sciences, Social and Behavioural Sciences, Engineering Education, Human-Computer Interaction or a
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parental leaves, military service etc.) Excellent/good skills in oral and written English. English is used as the language of instruction and supervision in this position. Expectations: A formal background