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Field
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by: Developing specialized algorithms supported on solid theoretical foundations and with a focus on challenging aspects of very high-dimensional datasets, such as datasets encountered in
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in the network. Here unfair indicates that people with different personal traits are differently and unjustly affected by algorithms not designed to consider those traits. This project aims to develop
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SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to kerstin.achtruth at tu-dresden.de or to: TU Dresden, Chair of Algorithms, Prof. Dr. László Kozma, Helmholtzstr. 10
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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Research Studentship in Neural Engineering 3.5-year D.Phil. studentship Project: Sleep classification for implanted neurostimulation systems Supervisors: Dr. Joram van Rheede, Prof. Timothy Denison
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is provided by Prof Mike Allen and SeaGen, a blue-tech company based in the South West of the UK, who will support product development and route-to-market strategies. Together, the supervisory team
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Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 2 months ago
Interactions” to develop datasets and algorithms to capture and analyze eye gaze. About the project We are supporting the development of highly realistic human and animal avatars for use in research, film
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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adaptation of the mesh during simulation to resolve and track features in the flow. The focus of your PhD would be on developing novel algorithms to efficiently redistribute and rebalance the parallel
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning