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Science, focusing on high-performance deep learning for neural implicit reconstruction of ultrasound data. The goal is to advance the scalability and efficiency of neural radiance fields (NeRFs) and related
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-class staff have reliable, high-quality IT systems to perform their work effectively. Location: 13 Banbury Road, Oxford Salary: £35,681 - £41,636 per annum (if part time, pro rata) Contract: Full Time
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Services Department as Team Lead (Endpoint Device Management Services), working hybrid. IT Services is a key department that ensures that the University has robust, reliable and high-performing IT facilities
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, or high performance computing would be desirable, as would any experience designing or delivering training courses. This post is offered as an open-ended position subject to external funding. It is full
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to the Head of Business Administration, you will oversee a team of HR professionals delivering a responsive, high-quality HR service across performance, employee relations, recruitment and staff wellbeing. You
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. Concurrently, you will develop lower order analytical models and perform high fidelity computational simulations to corroborate experimental findings and propose other configurations to be subsequently
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applicant. Access to high-performance computing facilities and cloud-based quantum hardware will be provided to support simulation and verification of theoretical methods. About you The successful candidate
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, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale genomic data analysis. You will be able to demonstrate how to organise and prioritise work
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the College’s research programme, as well as being able to teach high-achieving graduate students. Additionally, they will possess excellent oral and written communication and interpersonal skills and
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environments Technical literacy sufficient to engage with mathematical/computational work Why Topos UK Exceptional Team: Collaborate with world-leading researchers and high-calibre funding agencies. Inclusive