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27.04.2021, Wissenschaftliches Personal The Chair of Computational Modeling and Simulation (CMS) at Technical University Munich invites applications for the position of a Research Assistant (m/f/d) for performing fundamental research in the frame of the project AI-CHECK funded by the...
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
interfaces. Topics of interest include: Planar and geometric graph algorithms Approximation and parameterized algorithms Clustering, embeddings, and structural graph theory Computational complexity and
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well as an independent researcher. Our work is interdisciplinary, international, and in-depth, but also practical. We offer a possibility to obtain broad and profound expertise, both theory and practice, in the field
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researcher. Our work is interdisciplinary, international, and in-depth, but also practical. We offer the possibility to obtain broad and profound expertise, both theory and practice, in the field
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on an important topic in a well-funded multi-disciplinary international training network. The training involves multiple activities, in addition to your research, and secondments across our partners. Overview
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will be responsible for developing solutions in terms of methodology (theory) and implementation (application) of the new approach. Coordination of the cooperation with the project partners Publication
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. The project focuses on developing information theory, coding schemes, and other algorithmic methods for DNA data storage. Here is a video on the topic: https://www.bbc.com/future/article/20151122-this-is-how
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of the Technical University of Munich. In our group, we advance ethical practice and theory in medicine, bio-medical technology, and public health, driven by the belief that embedding ethics is essential for shaping
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of AI. The ideal candidates will have a background in computer science, statistics, mathematics, or related fields, as well as an interest in social science research methods and theories. The PhD
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: You will study the effects of privacy-preserving machine learning on memorisation, fairness, interpretability and model uncertainty utilising techniques from information theory and quantitative