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- Delft University of Technology (TU Delft); Published 11 Nov ’25
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research associate position in AI for science. The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will
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available from 01/04/2026 to contract end date of 07/03/2027. There have been many high profile achievements of Artificial Intelligence (AI) algorithms in recent years, demonstrating their effectiveness
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such an environment and using this information in control algorithms to ensure robust navigation. This project is funded by the Hauts-de-France Region. Mission The recruited person ensures research work. She will be in
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analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission management platforms, contributing to the deployment of smart UAV demonstrators under
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research subject for this position is development of distributed processing strategies and algorithms for Large Intelligent Surfaces, including both joint baseband processing and synchronization across
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systems, with a focus on 3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation
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that are selected by the special algorithm elaborated at ICCVS. The first in vitro and in vivo studies are very encouraging, and the team plans to start clinical trials in a few years from a prospective perspective
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or equivalent research experience in bioinformatics, computational biology or related subjects; should be familiar with state-of-the-art NGS tools, algorithms and pipelines for processing and
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conducted implementations of algorithms and simulations using contemporary GPU hardware, or Profound knowledge and experience of the DUNE software environment (https://dune-project.org/ ) (knowledge and
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will