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in simulated environments and with real data on real UAVs. Defining and calculating measures for levels of trust in the developed algorithms is essential. These uncertainty-aware algorithms can self
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to clients, and carers’ scheduling to allocate the time for clients’ visits. Various objectives and requirements of the variety of home-based services have to be taken into account considering carers’ and
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please consult the person specification and job description. The closing date for this post is 5th October 2025, with interviews scheduled to take place on 4th November 2025. Customer advert reference: B04
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considering applications and scheduling interviews as soon as they are received This post will remain open until the vacancies are filled
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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machine vision algorithms. The system will be designed with the physical constraints of remote fusion environments in mind, including radiation tolerance, restricted access, and the need for automation and
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argon. The analysis of the ProtoDUNE data will help to validate calibration techniques and particle identification algorithms. The candidate should have a good knowledge of particle physics and experience
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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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of accurate, accessible data to support annual reporting schedules, projects, and information requests. Working with colleagues across POD, Systems Development, and the wider university, you’ll ensure reporting
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submitted on the Apprenticeship programs, including preparation for End-point Assessment process. You will be responsible for your diary management of booking Tripartite reviews and scheduling in line with