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                Employer- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- Delft University of Technology (TU Delft); yesterday published
- Leiden University
- University of Groningen
- University of Amsterdam (UvA)
- Eindhoven University of Technology (TU/e); Eindhoven
- Leiden University; Leiden
- University of Amsterdam (UvA); Amsterdam
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Maastricht University (UM)
- Radboud University
- University of Twente (UT)
- University of Twente (UT); Enschede
- Delft University of Technology (TU Delft); today published
- Eindhoven University of Technology (TU/e); 4 Oct ’25 published
- Eindhoven University of Technology (TU/e); today published
- Eindhoven University of Technology (TU/e); yesterday published
- Maastricht University (UM); 27 Sep ’25 published
- Radboud University Medical Center (Radboudumc); Nijmegen
- University of Groningen; Groningen
- University of Groningen; 26 Sep ’25 published
- University of Twente
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- Utrecht University; Utrecht
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                specialist collaborator to guarantee adequate integration of perception and action; advanced motion-planning and control algorithms, continuously refined via robotic digital twins, enable reliable handling 
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                new generation of perceptual foundation models by contributing advanced perceptual pre-training and fine-tuning algorithms. What you will do You will carry out research and development in the areas 
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                -tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to 
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                sufficiently “compact” (i.e. algorithmically small and computationally efficient) to enable incorporation in integrated PED models. The development of these compact models will involve collaboration with several 
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                . The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data analysis. For more 
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                of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data