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the 01.10.2022. Your Responsibilities: You will work at the cutting edge of privacy-preserving deep learning research with a focus on one or more of the following topics: - Optimal model design for differentially
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10.01.2022, 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
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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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
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academic supervision from Prof. Henkel. You will participate in the doctoral program of the TUM School of Management; after about a year, there is the possibility to apply for the School’s Academic Train-ing
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, metal physics, or a similar degree Experience in software engineering (incl. high-throughput computing) Knowledge in the field of materials engineering of metals and materials modelling (i.e. CALPHAD
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Max Planck Institute for Plant Breeding Research • | Koln, Nordrhein Westfalen | Germany | 2 days ago
Full-time / part-time full-time Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline There is one application call per year. Details on the timeline can be
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investigation of the aerodynamic performance of advanced future compressor stages, support-ed by numerical modelling and simulations of performance-enhancing design features. In this research project you will be
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29.04.2021, Wissenschaftliches Personal This PhD position is in the field of resource management for wireless network that leverage “digital twins” modeling aspects of the physical (such as user
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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and