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the area of end-to-end modular autonomous driving using computer vison and deep learning methods. This includes developing an efficient and interpretable image processing, vision-based perception and
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and develop an optimal (efficient, cost effective, easy to use) technological operational method for detecting and estimating wallaby populations and dynamics. What you’ll do: Develop a technological
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questions about your research, your methods, your motivation and the potential impact of your research. See the selection criteria below and please read these thoroughly, bearing them in mind when answering
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many-body methods for multi-valence-electron atoms, with a focus on transition metals of interest for spin-crossover metal-organic frameworks (MOFs). The applicant will be involved in the development
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methods for provable network security. The School of Computer and Mathematical Sciences is recruiting a research fellow to work on next generation network security technologies. Join a world-class research
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technology and computer literacy, including email communication, basic AV equipment operation, and online form completion. Effective communication skills to interact professionally with students from diverse
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for this role include: Conduct research in the area of High Dimensional Approximation, Uncertainty Quantification, Deep Learning, and Quasi-Monte Carlo Methods independently and as part of a team, including
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for livestock management and fodder production. Effective communication skills that will enable you to liaise with a variety of university staff, students and clients. Current driver’s license Basic computer
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or professional qualification with relevant technical experience (>5 years) Demonstrate experience in computer numerically controlled subtractive programming, simulation and operation and other manufacturing
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) GRADE 7, £39,355 - £45,413 pa Fixed-term Ref: 095593 A Research Fellow position is available in the group of Professor M. J. Rosseinsky OBE FRS to work in a team of computer scientists and materials