62 advance-soil-structure-modeling PhD positions at Technical University of Denmark in Denmark
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passionate, curious, and driven individual, committed to accelerating the green transition by advancing autonomous solutions for wind power operations. You thrive on solving complex problems, ask bold and
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a team of 25 colleagues dealing with CCUS, and industrial partners in Denmark as well as abroad. Your primary tasks will be to: Apply AI in context of capture solvent modelling Understand and analyse
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; characterization of supramolecular polymers, their assembly, triggered disassembly, structure and properties. Active dissemination of the results obtained, in scientific articles and through participation in
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R Experience with single-cell RNA-sequencing, in particular analysis of data would be an advantage Experience with mouse models and possession of a FELASA B certificate would be an advantage as both
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advanced materials. Experienced in both strain development and related modelling & data analysis. Experienced with processes of biomanufacturing, including fermentation, downstream processes and scale-up
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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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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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system designed to safeguard underwater infrastructure. This is a unique opportunity to dive deep into advanced robotics research, gain hands-on experience with real-world testing, and make your mark in a
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on the homepage, www.healthtech.dtu.dk/research/research-sections/section-idun ) at DTU Health Tech. In the group, we are exploring and developing targeted and advanced delivery systems to be able
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functional priors from billions of years of evolution; how to compress measurements with controlled mixtures of molecules; and how to align models of laboratory experiments with observational human biology