632 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" uni jobs at University of Sheffield
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, personalised treatments, and systematic evidence verification. This is a complex information integration problem, where clinicians must analyse vast, heterogeneous, and fragmented clinical knowledge. Clinical
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Digital and sensor based conformance validation for large scale forged components (C4-AMR-Crawforth)
intermediary data streams that can offer insight into how the component and manufacturing process is performing. Within both of the fields of forging and machining there are numerous industry-ready low-intrusive
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-quality documentation. Essential Application/ interview Experience of working with confidential and sensitive information with discretion and professionalism. Essential Application/ interview High level of
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or fingertip, to detect changes in blood flow. These changes create a waveform that contains valuable information about the heart and blood vessels. While some researchers have used PPG to estimate blood
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Professional staff, you will be expected to demonstrate a commitment to the professional behaviours set out in the Sheffield Professional Framework. Please follow this link for further information: Sheffield
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the project's social media and website, supporting the leadership team's communication with funders, and maintaining all necessary management information and records (e.g., timesheets, project documentation
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environment. Desirable Application Further Information Grade Grade 4 Salary £25,249 - £26,707 per annum (pro rata) Work arrangement Part-time (80%) Duration 1 April 2026 - 24 December 2026 Line manager
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of driving continuous improvement, change and operational excellence. For further information, you can view the candidate brochure here: Uni of Sheffield - Head of ElectricalServices
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applicable in real time or without availability of case-specific data in terms of machine types. To address such problems, recent works have proposed machine learning techniques and data which can be easily
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for designing everything from longer-lasting batteries to more effective medicines. However, a major roadblock exists: understanding the complex data from these experiments is a slow, manual process that can