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inexpensive microfluidic techniques. While the mechanism of these drops’ motion is well-known, controlling their trajectory is often challenging. There exist experimental proofs-of-concept that utilize imposed
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mathematical foundations needed to make deep operators reliable, robust, and applicable for control of complex engineering systems. In this PhD project, you will investigate how operator-learning models can
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on solving challenges at the interface of Synthetic Biology, Control Engineering, AI, and Robotics, and their application to global challenges across biotechnology. The approaches taken will depend
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Are you fascinated by controlling magnetic matter by femtosecond laser pulses, eager to explore the underlying physical mechanisms, and passionate to develop a generic tool to ‘print’ complex
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Are you excited about the future of wearable healthcare? Join us to merge microfluidics and contact lens technology, developing smart lenses that enable controlled tear fluid collection and analysis
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» Nuclear engineering Engineering » Control engineering Physics » Neutron physics Engineering » Mechanical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application
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total appointment, averaged over the course of your PhD. Job requirements The successful candidate has the following qualifications: An MSc. degree in systems and control, fluid dynamics wind energy
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Fotograf Morten Hjertø 4th October 2026 Languages English English English The Department of Ocean Operations and Civil Engineering has a vacancy for a PhD Candidate in Control Systems Nuclear
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the cell response. A key innovation in this PhD project is to engineer environmentally (cell)-instructive hydrogels with controlled architecture (pore size, network interconnectivity) and mechanical
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PhD position to research novel fast controlled photonic integrated switches for low latency and highly scalable AI compute clusters. The electro-optical communications (ECO) group in the Faculty