36 programming-languages-"the"-"CNRS"-"Humboldt-Stiftung-Foundation"-"U" PhD positions at Technical University of Munich in Germany
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to the following programs and mention Niki Kilbertus as possible supervisor: If you plan to start before May 2026 (and only then), send your application as a single PDF in English to niki.kilbertus@tum.de with
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, or a related discipline Interested in climatology/meteorology as well as quantitative methods Prior experience in programming is a plus (e.g., using R or Python) Good communication skills and a high
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knowledge of quantitative methods, particularly in statistics and econometrics; experience in machine learning is a plus Background in business/management/behavioral science Experience with programming
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. Student / Junior Researcher Microeconomics / Applied Economics The initial appointment will be 2 years with the possibility of extension. Salary is in accordance with the German State Regulated Public
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tools (e.g., Python programming) is an advantage. You enjoy working in an international team and have good communication skills. Proficiency in spoken and written English is required. For more information
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or for multiobjective optimization problems. Implement the developed algorithms (e.g., in Python) and evaluate their practical performance on artificial and/or real-world data. Teach tutorials (in English) for
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of seismic methods and numerical simulations, Good PC and programming skills (e.g., with Python, MATLAB), Experience with measurement techniques and field measurements using sensor technology (ideally using
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simulation software, e.g., SUMO, MATSim, etc. Good command of a statistical or procedural programming language such as R, python, julia, matlab, etc. Interest in transport policy We offer We offer a full-time
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architectures, capable of capturing the structure of complex, high-resolution NMR spectra – analogous to how language models such as ChatGPT learn the structure of human language. One of the primary goals is to
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning