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Field
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developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the
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. For more details, please see: https://www.ukri.org/what-we-do/developing-people-and-skills/epsrc/studentships/industrial-case/ . Supervisor(s): Prof. Mike Antoniou and Prof. Marina Gashinova
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system complexity. Your work will include: Developing modular, efficient, and transparent control algorithms. Combining model predictive control with learning-based motion prediction under uncertainty
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Description The Chair of Transport Modelling and Simulation at TUD Dresden University of Technology is investigating models, algorithms and simulations to achieve an enhanced scientific
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) for developing more reliable systems for Railway Transportation and contributes to Green Transportation. The goal is to identify early signs of wear or faults of the railway power system to improve maintenance
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control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance the safety and robustness of human-robot interaction. The successful applicant will engage
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candidate to fill one Research Professional 5 - Educational Research position. The position will primarily focus on building large datasets and developing algorithms to detect cognitive/affective states
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decision with multiple data sources. One example is to develop the semi-supervised methods and dynamic system interfacing algorithms to produce an automated and real-time information exchange across
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aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimising PIC algorithms for modern
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to the future upgrade of the CMS experiment. In particular, you will take a leading role in the development of the upgraded Tracker backend and Level-1 Trigger systems. Your focus will be algorithm development