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(e.g., Reinforcement Learning, Agent Based Modelling) to join our team full-time as part of a large international collaboration of European researchers (incl. Tobias Dienlin, Veronica Kalmus, Adrian
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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) Development of an Augmented Smart Classroom for Personalized Learning (SmartClass) serving as a test-bed for the collection and analysis of students and professors data, leveraging on data analytics and machine
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on qualifications and relevant skills acquired and will also be determined by the funding available. About you You should hold a PhD/DPhil, or be close to completion, of a relevant PhD/DPhil with a proven record of
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A PhD in immunology or related field with a deep understanding of mucosal immunology is required. Expertise working with primary human cells, intestinal tissues/organoids, murine models of intestinal
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of reports, publication of results Your qualifications: Completed university studies (M.Sc./ M.Eng.) and PhD in polymer science or a comparable field of study Relevant experience in thermal analysis and fire
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given to those with the ability to express and purify myosin proteins. Minimum Qualifications (Knowledge, Skills, Abilities, Education, Experience, Certifications, Licensure) A PhD in Kinesiology or a
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Essential Qualifications Applicants require a PhD in a Biochemistry-related field Preferred Qualifications: Experience with animal behavioral studies, advanced statistics, and experimenta design
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profile PhD in Mathematics, Theoretical Computer Science, Information Theory, Physics or related fields High level of mathematical maturity Experience with topics related to quantum LDPC codes and decoding
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interpretation of atmospheric circulation in high-resolution reanalysis data, idealised model simulations and a state-of-the-art weather forecasting system. The post-holder will have the opportunity to teach