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Field
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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the need for sustainability to achieve Net-Zero goals. Cyber-Physical Systems (CPS) integrate machines, robots, and AGVs, but challenges like mechanical wear and electronic errors pose risks to efficiency
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/electrical engineering, or a related field. Strong interest in automation and laboratory robotics. Basic experience in wet-lab microbiology techniques. Willingness to learn programming and computational
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-edge advancements in automated drug discovery through the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning
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, lasers, quantum photonics, optical sensors, LEDs, photovoltaics, ultra-high speed optical transmission systems, bio-photonics, acoustics, power electronics, robotics, and autonomous systems. Technology for
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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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Mechanical, Production, Materials, Industrial or Robotics Engineering, Computer Science, or related discipline, or a similar degree with an equivalent academic level. Approval and Enrolment The scholarship
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Supervisory Team: Dr N.C. Townsend, Prof A. Murphy PhD Supervisor: Nick Townsend Project description: In this PhD project you will explore novel wave energy harvesting systems for maritime robotic
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that support spike-based processing and memory-efficient computation using SSMs, targeting edge-AI scenarios in wearables, robotics, or sensor networks. Research area and project description The project will co
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skills as well as good communication skills in English. You should have completed a two-year master's degree (120 ECTS points) in Mechanical, Production, Materials, Industrial or Robotics Engineering