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Field
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, developing groundbreaking algorithms and systems that mimic natural processes to elevate the capabilities of robotic systems. In this role, you will engage in transformative projects that utilize bio-inspired
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streets of Edinburgh. These annotations will provide ground-truth information for machine learning algorithms, contributing to our research and development efforts to develop safe human-centred AI
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diagnostics and antimicrobial resistance (AMR) profiling. Work at the interface of engineering, data science, microbiology and clinical research. Become part of a larger consortium together with Prof. Emma
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AI solutions? We are launching a pioneering research and innovation hub in AI-one that will shape the way humans and machines collaborate for decades to come. Led by Prof. Usama Fayyad, the Institute
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challenge meeting this requirement is the simultaneous need for low-power consumption. The main objective of the project is to develop a complete end-to-end high-performance DNN system for on-premise
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in our mission to develop clinically useful algorithms, drive high-impact publications, and pave the way for personalised breast cancer treatments. Analyse data from cutting-edge technologies
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motivated Post-Doctoral Associate to join our team with a strong background in robot control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance
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. Responsibilities include (but not limited to): Lead the development of the NC-ARPES technique (hardware, post-processing algorithm, theory, data interpretation) Propose and perform new TR-ARPES studies of quantum
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. The overall aim of this project is to address these challenges by: Developing new data-driven and physics-based models of battery behaviour. Designing advanced BMS algorithms for real-time monitoring and
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. This role will involve the development of novel lattice QCD algorithms and high-performance computing (HPC) codes, and/or exploring applications of artificial intelligence (AI) to lattice simulations