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Field
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have an excellent Researcher Development programme, and wider institutional postgraduate training. We are committed to supporting a strong and growing PGR community, including PGR-Led activities
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advanced seismic methods (including array processing, machine learning, and potentially distributed acoustic sensing) to develop novel approaches for monitoring unsteady and non-uniform flood flows across
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differential testing) We focus on techniques that apply to real-world software systems. E.g., in the past, we have developed techniques that find and fix bugs in widely used Python, Java, C/C++, and JavaScript
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seeking for motivated candidates that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon models and
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. The aim of this project is to develop multimodal generative AI for embodied collaborative agents. This project aims to create AI agents capable of seamlessly collaborating with humans and other agents in
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technologies for people with disabilities. The TU Delft Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS) invites applications for a PhD position aimed at developing advanced AI-driven
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and to develop a novel methodological framework to design effective and fair personalized policies. The project will include large-scale field experiments in collaboration with company partners. Strong
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of Pharmacy. The project will focus on the synthesis of polymers that resist the formation of biofilms and the development of processes to formulate / process them into new health/consumer care products e.g
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Published Wednesday 19 Nov 2025 Deadline Monday 15 Dec 2025 Work area Research, development, innovation Organisational unit Erasmus School of Social and Behavioural Sciences (ESSB) Salary € 3.059
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | about 1 month ago
, startups and patient partners. Your Mission as PhD is to develop novel machine-learning methods to (1) detect small, clinically meaningful changes in joints, (2) enable robust longitudinal monitoring and