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PhD Studentship: A continual learning approach for the development of robust robotic control systems
A continual learning approach for the development of robust robotic control systems This project is an exciting opportunity to undertake industrially linked research in partnership with
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properties of superconducting circuits, both analytically and numerically. Familiarity with open quantum systems. Background in optimal control methods. Experience with machine learning for optimization
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Status: Open Applications open: 30/10/2024 Applications accepted at any time View printable version [.pdf] About this scholarship Description/Applicant information Applications are open for the 2024 HDR Scholarship in flow assurance and corrosion mitigation at Curtin Corrosion Centre. Curtin...
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PhD Scholarship in Design optimizer tool and predictive control system of a Megawatt Charging System
deliver cutting-edge research, provide top-notch designs and developments on power electronics, powertrains and smart green energy solutions with intelligent control systems for tomorrow's mobility
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. The control systems developed will be designed to handle these complex dynamics, feeding directly into industrial SBSP and related designs, thus enabling their longer-term plans for commercial SBSP capable
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The SnT Automation & Robotics Research Group is hiring a motivated PhD candidate for the bi-national project DOMINANTS (Dexterity-Oriented Methodology in Optimized Design and Control of Soft Aerial
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The DFG-funded Research Training Group (GRK 2717) „Dynamics of Controlled Atomic and Molecular Systems (DynCAM)“ at the University of Freiburg in collaboration with the University of British
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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-centred operation (NCO) information, including positions, threats and airborne warning and control systems (AWACS). Streamlined and effective decision making in complex scenarios. Natural interaction and
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and grippers offer improved safety and adaptability but introduce new challenges in design and control. Their development is still largely bio-inspired and trial-and-error based. Integrating flight and