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. This project aims to develop machine learning models to predict a particle shape and size for a given chemical formulae and crystallisation method. Extractive Language learning models developed will be able
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, direct electroreduction (electrolysis) and plasma synthesis. Correlating experimental, ab initio and multi-scale simulation as well as machine learning techniques is central to our mission: Development and
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, investment casting processing, computational modelling of casting processes at a macro-scale / component level and at a dendritic growth level, and machine learning methods for process optimisation. The ARCANE
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Localization Microscopy (SMLM), particle tracking and cryo-electron tomography (cryo-ET). The measurements will be integrated using machine learning and data modelling. Our lab combines cell engineering and
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to announce a joint open PhD position in Improving Machine Learning Methods for Contingent Claim Pricing and Hedging. The project will focus on, but will not necessarily be limited to, a promising relatively
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7th June 2024 Languages English English English The Department of Physics has a vacancy for a PhD position in Two-phase flow in confined geometries Apply for this job See advertisement This is NTNU
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interfacial properties, colloidal interactions) and computer simulations. Of particular interest are designing molecules, particles, and interfaces to perform a specific function, and to develop novel
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techniques based on machine learning with deep neural networks (DeepSets) using GPUs. Data and tools from the LHCb experiment will be utilized. Additionally, contributions to the data analysis of LHCb
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(mean-field computations on bulk phase behavior and interfacial properties, colloidal interactions) and computer simulations. Of particular interest are designing molecules, particles, and interfaces
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of Energy and Process Engineering has a vacancy for a 3-year PhD position in indoor environment quality, airborne microbial pollutant and ventilation in hospital environments. The position is in the frame