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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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or equivalent Skills/Qualifications Holding a degree of Master of Science [Magister], Master of Engineering [Magister Inżynier], medical doctor or equivalent in the field of: exact sciences, natural sciences
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18 Jul 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Engineering » Chemical engineering Engineering » Process engineering Researcher Profile
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for data-efficient vision foundation models. Foundation models in computer vision currently rely on massive datasets and brute-force scaling. This leads to high data requirements, hidden biases, limited
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Requirements . Please note: You can apply online. We will not process applications sent by email and/or post. As part of knowledge security, TU Delft conducts a risk assessment during the recruitment
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. This knowledge is required to better understand the processes involving copper signaling in health and disease. Biological compartments, relevant to copper signaling, including synapses, have very high surface
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, scalable energy concept in which the computing infrastructure and energy system are tightly coupled and jointly optimized. During the project, a container-scale prototype will be realized, which will combine
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Selection procedure for admission to the PhD programme in
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, engineering, mathematics, physics or related field; strong programming skills; interest in ML, computer vision, robotics, embodied AI or autonomous systems; motivation for independent research and high-quality
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a key challenge in sustainable process engineering: translating microbial electrolysis cell (MEC) technology from lab-scale innovation to robust, industrial deployment. The research will sit at