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well as their cleanroom fabrication by silicon micromachining will be investigated. The main challenges are (1) the design and modelling of new sensor topologies, (2) development of the MEMS fabrication processes, (3
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
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main research topic for this position is to investigate the thermal break-up of pure polymers and composites using a combination of experimental techniques and chemical modelling. For a position as a
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to the PhD project ie. processing and analysis of dietary intake data, statistical analyses (eg. linear mixed models) as well as evaluation of child growth and body composition data. Relevant publications
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
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for the new green steels compositions, including impurities and tramp elements. These models should enable density-functional-theory (DFT) accurate large scale atomistic simulations of defects including
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areas of the group is investigation of the fate of vitamins and other nutrient due to processing of foods, particularly in the context of ultra-processed and plant-based foods. The group also contributes
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responses and recovery in humans. To this end we combine data from psychopharmacological experiments in healthy participants and real-world clinical data from surgery patients for a more in-depth
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of bio-oil compositions Developing semi-automated and validated data processing tools and workflows for one- (1D) and two- (2D) dimensional chromatography data Correlating bulk analysis with the more