411 computer-programmer-"IMPRS-ML"-"IMPRS-ML"-"IMPRS-ML" positions at University of Sheffield in United Kingdom
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. Identify, create and provide a training and development programme tostaff on contracts and contract negotiation. Develop and implement an efficient contracting process, including an appropriate, transparent
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programme This University of Sheffield PhD project is part of the EPSRC Centre for Doctoral Training in Sustainable Sound Futures programme. Further details about the CDT and the programme can be found
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of cost centres and external project accounts using the finance system Plan tasks in accordance with known deadlines, including external funder requirements, monthly and annual University financial
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GIF CDT: A Novel Gas/Liquid contactor for Direct Air Capture and industrial CO2 capture technologies
applications for the following project. This advert will close when a suitable candidate is identified, so early application is encouraged. The CDT boasts an exciting and challenging programme specifically
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Ability to assess and organise resources, and plan and progress work activities, both individually and in conjunction with other team members. Essential Interview/Application Proven ability to prioritise
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of delivering presentations. Essential Application/Interview Ability to develop creative approaches to problem solving. Essential Application/Interview Ability to assess and organise resources, and plan and
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/MSc or equivalent in Computer Science or Software Engineering (assessed at: application/interview). A track record in software testing, testing for autonomous systems, or autonomous driving system
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application statement. Essential criteria Must have a technical background with experience in the software development lifecycle. Previous experience as a software engineer or developer is crucial (assessed
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Application/interview Good working knowledge of a relevant computer programming language, for example Matlab, Python or C/C++. Essential Application/interview Good working knowledge of software packages
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”. This exciting opportunity involves leading the development of advanced data-driven mathematical and computational models to suppress turbulence in pipe flows, contributing to pressing engineering efforts toward