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. Zhang, N. Ledos, Y. Prado, X. Lafosse, C. Abadie, S. Ithurria, G. Vincent, Y. Todorov, C. Sirtori, A. Vasanelli, E. Lhuillier, Nano Lett 23, 8539 (2023) 3. Electric Field Distribution within a Van der
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in the design, implementation, and maintenance of data pipelines and leading/assisting in building algorithms for deep learning with close collaboration from the study team. While programming
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optimization algorithms for both estimation and inference. Special emphasis is placed on advancing statistical approaches to address the demands of increasingly complex, high-dimensional, and unconventional data
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performance. Some suggested research topics may include: (i) NISQ algorithms, (ii) quantum simulation of complex many-body systems, and (iii) quantum computational complexity. This list is not intended to be
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the coordination of large-scale robot systems (ground and aerial). The ideal candidate will possess hands-on experience with designing and implementing reinforcement learning algorithms, and deploying them onto real
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programming of algorithms. The use of programming languages such as Python, R, SQL, and C++ will be a daily part of the project, and proficiency in these languages is required. However, additional datasets will
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singular elliptic and parabolic PDEs, free boundary problems, optimal control of free boundary systems with distributed parameters. Current areas of interest include Potential Theory, Harmonic Analysis
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possession or distribution of material, reagents, or records belonging to their laboratory and any proposed disclosure of findings or techniques privately or in publications. Collegial conduct towards co
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algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our website: https://www.math.cit.tum.de/en/math/home/. The position is a full-time position (100%), initially
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supervision conferring with higher levels only in unusual situations. 8. Performs other job-related duties as required. Responsibilities: -Develop and implement advanced numerical algorithms and deep learning