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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- University of Bergen
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); today published
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Field
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edge AI hardware/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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. The successful candidate will be based in Odense, under the primary supervision of Prof. Ricardo J. G. B. Campello , but they will be expected to also work closely with other PhD students, postdocs, and
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Manufacturing and/or AI Robotics. Focus areas include: yield/defect causal analysis and XAI; process-variation monitoring and root-cause analysis; intelligent scheduling/dispatching for WIP/throughput (multi
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to the research; Good understanding of computer architecture; Basic understanding of MRI algorithms is a plus; Understanding of AI and its practical implementations; The ability to work in a team and take
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Network: ENDOTRAIN (Digital Endocrinology Training Network to Combat Adrenal Diseases) , and is scheduled to start in August 2026. The position is funded by European Union’s Horizon Europe research and
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Adrenal Diseases) , and is scheduled to start in August 2026. The position is funded by European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie (Grant Agreement
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contribute to the development of novel algorithms and methodologies that enhance the robustness and accuracy of acoustic measurements. We are looking for a highly motivated candidate, with a relevant MSc
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at the Institute of Visual Computing, course on Fundamentals of Geometry Processing, or Data Structures and Algorithms. Co-supervision of bachelor and master thesis. Research and development
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availability, more attractive working hours at service locations, and decreased train unit movements for regular maintenance. New innovative approaches need to be developed to schedule rolling stock