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Funding Source: DLA award Eligibility: Available to home fee status and UK domicile EU students We are seeking an enthusiastic PhD candidate to join the Centre for Polymers and Composites (CPC
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are looking for a motivated and talented PhD candidate to join a unique interdisciplinary project at the intersection of machine learning and formal methods. Information Machine learning models deployed in real
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PhD: Characterization of subsurface heterogeneity in backward erosion (piping) Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application
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(e.g. Agentic Reinforcement Learning), evaluation, tool use, agentic harness, or retrieval-augmented systems. Internship/full-time experience from research, engineering, or algorithm-development roles in
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preferably quantum machine learning. Strong interest in sustainability, education, and youth development. Key Responsibilities: Apply reinforcement learning to optimise resource allocation in hydroponic
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Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 31 Aug 2026 - 23:55 (Europe/Amsterdam) Country Netherlands Type of Contract Permanent Job Status Full-time Hours Per Week 40
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components. You will explore how learning-based methods, such as imitation learning and reinforcement learning, can be integrated with model-based low-level controllers and multimodal sensing to enable contact
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areas of nanoscience and nanotechnology. Job Title: PhD Student - Nanobioelectronics and Biosensors Group Department: Nanobioelectronics and Biosensors Description of Group/Project: The Nanobioelectronics
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commissioning at installation, the system will continuously recalibrate itself in response to changing operating conditions. Reinforcement learning is envisaged as the primary methodological vehicle, enabling
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI