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part of the EU-funded HUM.AI.N-ACCENT Doctoral Network, an interdisciplinary consortium of 13 labs and 24 partners across Europe, Canada, and the US. The successful candidate will benefit from
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or industry. Network-wide Training Events will provide complementary knowledge, multidisciplinary experience, and transferable skills: ethics & research integrity, scientific writing, communication
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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management experience across multiple industries, build an extensive professional network spanning academia and industry, and cultivate commercial awareness that enhances your career prospects. Alumni from
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and communication skills. Funding Self-funded/partially funded Please contact the supervisor for more information or explore our funding finder . Cranfield Doctoral Network Research students
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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The Medicines And Healthcare Products Regulatory Agency; | Canary Wharf, England | United Kingdom | 4 days ago
communication skills with experience in presenting research, writing study protocols, and drafting strategic papers and manuscripts for publication. Actively build and maintain a network of colleagues and
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filled. This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end. You will unify modern machine learning, statistical signal
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trust in digital communications and readily bypass conventional security controls. This PhD research proposes to design, develop, and validate a novel, explainable, multi-modal detection framework. By
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, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from nonlinear control and optimisation