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to scalable fabrication and integration of photonic sensing or actuation. The project is conducted within the Surgical Robotics Laboratory and the Department of Biomechanical Engineering at the University
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Department: Electrical and Electronic Engineering Title: Intelligent Distribution System Operation for Low-Carbon Power Systems Application deadline: All year round Research theme: Power and Energy
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of the Manufacturing Technology Centre (MTC) and academics within the Power Electronics, Machines and Control (PEMC) Research Institute , University of Nottingham. The project will be supported by the state-of-the-art
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application! We are looking for a PhD student in automatic control at the Department for Electrical Engineering (ISY). Your work assignments We are seeking a PhD student in automatic control for a research
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application! We are looking for a PhD student in automatic control. Your work assignments You will work within a project focused on optimization, planning, and decision-making in control systems involving
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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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) The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology
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application! We are looking for a PhD student in automatic control at the Department of Electrical Engineering (ISY). Your work assignments This PhD project is part of a close collaboration between Linköping
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research project. Advanced command of both spoken and written English is mandatory PhD fellowship to obtain a PhD in Chemical Engineering. We will support you in all aspects in order to successfully run your
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to robotics, autonomous systems, or maritime technology Experience with robotics software frameworks such as ROS/ROS2 Experience with multi-agent systems, optimalcontrol, or model predictive control Experience