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Field
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Location: University of Nottingham, Faculty of Engineering Start date: 1 October 2026 Application deadline: 24/07/2026 Project type: Collaborative PhD studentship (joint Academic-Industry
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actively on the preparation and defence of a PhD thesis in the field of explainable reinforcement learning (XRL). Explainable reinforcement learning aims to make decisions, policies, and learning processes
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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the Faculty of Applied Engineering is looking for a full-time (100%) doctoral scholarship holder in the field of Artificial Intelligence. Position You will work actively on the preparation and defence of a PhD
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Phd Studentship in Computer Science: Empirical Security Assessment of AI Decision Engines in Cyber-Physical Systems Award Summary 100% home fees covered, and a minimum tax-free annual living
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22.12.2025, Academic staff We are an interdisciplinary team at the Chair of Safety, Performance and Reliability for Learning Systems, and we are looking for exceptional PhD candidates to join our
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ARDITI - Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação | Portugal | 2 months ago
Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 11 Aug 2026 - 23:59 (Europe/Lisbon) Country Portugal Type of Contract Temporary Job Status Full-time Hours Per Week
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(including knowledge management), software development for and on smartphones and tablets, and intelligent autonomous robots. Furthermore, the institute deals with the teaching of artificial intelligence in
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17.05.2026, Academic staff In networks of robots and unmanned aerial vehicles, the ability to exactly coordinate the observations of the different devices is key for successfully carrying out
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16.05.2026, Academic staff For applications in networks of robots and unmanned aerial vehicles, reliable coordination is essential. Such systems are particularly vulnerable to jamming and