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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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will conduct sampling, and apply experimental methods such as metagenomics and metatranscriptomics, linked to soil and emission data to help create predictive models. Within a broader framework, your
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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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, electrolysis, power-to-x, batteries, and carbon capture. The research is based on strong competences on electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery, materials
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Job Description RISC-V open source and open standards as the nucleus for new platform models help to improve overall flexibility and productivity for a wide market access. Given the challenge
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of computational chemistry. Applicants can have a background from cheminformatics including RDKit, machine learning applied to chemistry, and molecular modeling Our group and research- and what do we offer? Our
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disease models. The emphasis will be on the communicative potential of these vesicles in the gut-brain-metabolic axis. The LHMI group operates a state-of-the-art molecular biology laboratory and maintains
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-objective optimization, and online decision-making approaches are relevant areas of expertise for this position. Previous experience with developing models, tools, or processes is desired. The candidate has
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. We are a team of currently ca. 15 researchers and engineers, specialized in robot solutions involving modelling, simulation, and control of advanced robot technologies (systems and services
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modelling approaches will also be used to complement the experimental work. The bioprocess engineering team at DTU Chemical Engineering consists of around 10 scientists and engineers. Expertise is available