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://mediatum.ub.tum.de/doc/1696192/aab7jokzk7x4paq7m2y9pa2p6.Wetzlinger-2022-NAHS.pdf Job Specifications For PhD applicants: Excellent Master’s degree (or equivalent) in computer science, engineering, or related
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Review, update, and consolidate methodologies, including Bayesian methodologies, in the context of material balance evaluation Your Profile: PhD in applied mathematics, computer science, physics, or in
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, or machine learning is also appreciated. PhD: The candidate is expected to have some background in theoretical computer science, including some of the following areas: automata, logic, games, verification
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) and present your work at top conferences and journals in our field. Candidates should have completed their Master/Diploma studies in Computer Science, Mathematics, Mechatronics, Electrical Engineering