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the supervision of dr. ir. Mathias Peirlinck and dr. ir. Aimée Sakes. The Peirlinck Lab integrates multimodal experimental data, physics-based modeling, and machine learning techniques to understand, explore, and
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mean that new technologies are urgently needed to limit micro-plastics release in the aquatic environment. A large contribution to micro-plastics pollution comes from microfibers released from clothes in
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specifically, you will study and develop algorithms for managing the robot's information based on these observations and based on a predefined, or learned, knowledge structure. Since our robots will operate in
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will receive a 1-year TU Delft contract with a potential for extension. Because we are reaching out to a large target audience, we will offer 2 different scales: Scale 10 (€3226 gross to €5090 gross
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Challenge: Harnessing and leveraging health data for analysis, interpretation, and decision making. Change: Combining conventional, data-driven, and AI-based methods. Impact: Drive the development
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for at least one application. The novel methods will only need to support the selected ADAS applications and should not extract more information than needed. By aiming for minimal hardware and computational
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of techniques to analyse the structure, (defect) composition and morphology of the films. Secondly fundamental understanding involves data interpretation and model development using knowledge of solid
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today, climate change without doubt being the most important. Our focal subjects: sustainable aerospace, big data and artificial intelligence, bio-inspired engineering and smart instruments and systems
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, our mission is to build prototypes of fault tolerant quantum computers and large-scale quantum internet. However, the technology is still in an early phase. In this phase, it is extremely important to
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(or related). Interest in robotics, artificial intelligence and data science. Demonstrable experience in symbolic planning, reinforcement learning, motion planning and/or Markov decision processes. Solid