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on a new project called TRUSTLINE, which is part of the Learning Introspective Control (LINC) DARPA Program. The project aims to develop machine learning (ML)--based introspection and monitoring
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TRUSTLINE, which is part of the Learning Introspective Control (LINC) DARPA Program. The project aims to develop machine learning (ML)--based introspection and monitoring technologies that enable robotic
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as a primary discovery tool and translating key findings to more complex vertebrate neural circuits, we aim to uncover the unknown functions of kinetochores in these major neuronal processes
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candidate will help to support the team to formally document algorithms within a quality management system. EpiNav™ provides state-of-the-art computer-assisted support for the planning of stereotactic
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document algorithms within a quality management system. EpiNav™ provides state-of-the-art computer-assisted support for the planning of stereotactic neurosurgery procedures. It has been in daily clinical use
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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plasma-modulated plasma accelerators (P-MoPAs). The successful candidates will undertake research into the underlying physics and operation of plasma-modulated plasma accelerators (P-MoPAs), advanced
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with spin-orbit-coupled interfaces and superconductors can be used to generate and control unconventional superconducting states. In particular, the project aims to realise and manipulate electron
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clinical challenges within hospital settings. A deep understanding and hands-on experience in user-centred design, electronic circuitry, programming, and system controls within medical contexts are highly
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of controllable long-range dipole-dipole interactions, long trap lifetimes and strong coupling to electric and microwave fields. The project will explore the novel approach of using Feshbach resonances between Yb