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Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
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. Electrification alone is not enough. If we cannot control and optimize these systems intelligently, we will not unlock their full potential. This PhD is about contributing to these missing pieces. Expected starting
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Position Summary: Applications are invited for a PhD studentship, to be undertaken at Imperial College London (Control and Power Research Group, Department of Electrical and Electronic Engineering
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the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position in
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in all these approaches. This PhD position aims at developing methods for learning and control in complex large-scale systems. This will be carried out as part of the GreenControl project, whose
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
Job Code: Application for PhD-3: Insula Job Offer from August 21, 2026 How do internal bodily states shape cognitive control? This interdisciplinary project investigates how the anterior insula
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frequency stability. Building on previous achievements, the goal of this PhD project is to explore advanced control strategies to achieve the specified SRF cavity field stability (0.01% in amplitude and 0.01
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Infrastructure? No Offer Description PhD position in Modeling and Control of Nonlinear Dynamical Systems from Data The Chair in Nonlinear Dynamics at ETH Zürich is inviting applications for a 100% PhD position in
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that the estimation and control algorithms comply with automotive standards and are compatible with automotive protocols. They will lead EMB test bench development and provide the necessary training to the PhD student
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Modern autonomous ground vehicles (AGVs/UGVs) in defence operations require sophisticated AI/ML-based control systems for perception, decision-making, and adaptive responses in complex, unstructured