11 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" PhD positions at University of Exeter in United Kingdom
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The always-on, safety-critical nature of air traffic control raises rich and exciting challenges for machine learning and AI. The University of Exeter in partnership with NATS, the UK’s main air
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computer scientists to design paradigms that compare "active" learning (standard VR) against "proprioceptive" learning (haptically guided movement), measuring outcomes such as path efficiency, force
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this industrial PhD studentship in Physics – fully funded by the EPSRC and QinetiQ. We’re looking for a student who has a passion for science, with ambition to learn and apply their own ideas, perspectives, and
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with NEOM, one of the world’s largest ecological restoration programmes, the project will develop machine-learning approaches to analyse satellite observations of vegetation change and evaluate large
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data
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machine-learning-based surrogate models to accelerate design and control workflows. This PhD studentship would suit candidates with backgrounds or interests in engineering, physics, applied mathematics
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, adaptive control strategies, and hybrid energy storage solutions to address key challenges in self-powered systems under dynamic environmental conditions by: Develop machine learning or heuristic-based
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algorithms that combine Reinforcement Learning techniques like Partially Observable Markov Decision Processes (POMDPs) with cognitive inference modules capable of modelling human beliefs, intentions, and goals
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jointly learn from images and text, most current systems are still limited in three important ways: they primarily rely on statistical pattern recognition rather than structured clinical reasoning