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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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research in the field of modern high voltage polymer electrolytic capacitors, develop models for lifetime prediction, methods to predict and test for reliability, understand physics of failures at elevated
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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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spectroscopy (important). Experience with building UHV systems. Experience or a desire to learn about quantum device fabrication. Experience in modeling with 3D CAD like autodesk inventor. A strong grasp of
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could include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success Policy Implications and Recommendations
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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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portfolio. Experience with sustainable protein production, especially at the interface of protein and material sciences, including designing and expressing functional proteins for novel advanced 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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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