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Postdoctoral Research Associate (f/m/d) for an Interdisciplinary Research Project on AI in Education
with AI developers regarding subjective theories about "good" teaching strategies Planning and management of data collection in field studies Research data management Quantitative data analysis
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technologies to fundamental physics questions. The advertised positions will be part of the project “QS-Gauge: quantum simulation of lattice gauge theories”, funded by the Emmy Noether programme of the DFG
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position within a Research Infrastructure? No Offer Description Work group: IAS-2 - Theorie der Weichen Materie und Biophysik Area of research: Scientific / postdoctoral posts Job description: Your Job
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position within a Research Infrastructure? No Offer Description Work group: IAS-2 - Theorie der Weichen Materie und Biophysik Area of research: Scientific / postdoctoral posts Job description: Your Job
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spirit of global partnership. Today, around 23,000 people learn, teach, research and work on our international campus. In research and teaching, administration and operations, we are firmly committed
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through publications in relevant journals; solid quantitative/statistical background (e.g., probability theory, linear algebra, optimization) Strong expertise in at least one of the following areas, with
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Design and implement optimization techniques for full-stack improvement of quantum algorithms Model major sources of experimental error for control theory or for error mitigation techniques Scientific
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Learning Formal Methods Dynamical Systems Control Theory Context The applicant will be directly advised by Prof. Matthias Althoff (https://www.ce.cit.tum.de/cps/members/prof-dr-ing-matthias-althoff
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“Equilibrium Learning, Uncertainty, and Dynamics.” About the Project Market interaction is increasingly automated by artificial learning agents. Examples include pricing agents in electronic retail or bidding
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interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Control Theory Formal Methods Machine Learning Context The applicant will be directly advised by Prof