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machine learning classification of fluvial systems to generate a time-series of sediment erosion and deposition from optical satellite imagery. These data will be an essential component of the wider project
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including the following: Use of Machine Learning; Web applications; Exploiting the capabilities of the University’s High Performance Computing facilities particularly in data-intensive applications. The post
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or more of these technologies: ◦ Shared and distributed memory programming tools) (e.g. OpenMP, MPI, CUDA) ◦ Make and other build and installation management tools ◦ Machine learning
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to global agriculture. In this context, the EU-funded Sensorbees project is developing and combining micro-robotic, biological and machine learning technologies into a system that can support the well
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applicants with expertise in the fields of artificial intelligence, computer vision, edge computing, digital twins, human-computer interaction, user modelling, robotics, resilient computing, where
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. Candidates with strong track records in computational tropical geometry, non-archimedean analysis and geometry, machine learning, and related areas are encouraged to apply. Hybrid and flex working arrangements
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research, delivering tutorials or computer practical classes, and possibly some lecturing. Allocation of teaching duties will take account of the Fellow’s expertise and experience as well as the Department’s
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of a catalogue of material composition of satellites 2. Developing material degradation models and estimating the material properties based on life and degradation. 3. Developing machine learning
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computing, cloud computing, machine learning, artificial intelligence, programmable networks, digital twins, and cyber physical systems. Skills Demonstrable ability to work cooperatively as part of a team