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algorithms for optimal operation of grid-integrated LDES; Develop a co-simulation framework to analyse LDES performance under different grid scenarios. Collaborate with consortium partners to translate
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. This paradigm enables algorithms to securely access and process data within the environments where it resides, supporting federated learning for training machine learning models without moving sensitive or large
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deliveries). You will build a prototype algorithm that can be used by infrastructure and civil engineering professionals to better evaluate cable and pipeline location data. Your tasks will include: 1
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-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning algorithms that maximize the information extracted from
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algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement learning. Deep
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control algorithms lies a physics-based simulation model, whose accuracy largely determines the effectiveness of the control loop. Position 3 – High-fidelity simulation of the LAFP process Current
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that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon models and neural control pathways (e.g., CPG-like and
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at the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente in Enschede, The Netherlands, and several experienced supervisors (Dr. Ray Hueting, PE group and Prof
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learning and team learning in the HRD track of our Master and pre-master program Educational Science and Technology (EST). You will also contribute to the challenge-based interdisciplinary minor
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. The intended starting date is around April 1, 2026. Screening is part of the selection procedure. About the organisation The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses