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qualifications and experiences needed for entry at a particular level. Find out more about the Faculty of Medical Sciences here: https://www.ncl.ac.uk/medical-sciences/ Find out more about our Research Institutes
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astrophysics Machine learning or data analysis for physical sciences Strong programming skills (e.g., C/C++/Fortran and Python) and familiarity with reproducible workflows/version control. Motivation to pursue
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Research + Invention Agency’s (ARIA) Universal Fabricators programme (https://aria.org.uk/opportunity-spaces/manufacturing-abundance/universal-fabricators ). The project has a bold goal: to develop a new way
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information please visit: https://www.brunel.ac.uk/about/our-history/home The Department of Computer Science at Brunel, where this project will be based, has a strong record of internationally recognised
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CAD/Python/MATLAB. You are an excellent communicator, capable of working collaboratively in interdisciplinary teams and engaging with industrial partners. To find out more about what it's like to work
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curriculum management system: (https://falmouth.akarisoftware.com/ ). Key responsibilities will include: Curriculum Development: Shape and deliver the syllabus, driving the development of new Computing
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for Scotland (https://preventabledeathstracker.scot/ ), the first digital vigilance platform that enables learning from medico-legal death investigations. The Preventable Deaths Tracker (PDT) is a relational
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demonstrate: Good programming skills in Python and experience developing modern web applications using React.js. Experience working with SQL databases and an understanding of modern web application architecture
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should be proficient with Python, the Pandas data analysis library and a data visualisation library such as Matplotlib, Seaborn, Plotly etc. Also Desirable: Proficiency with Text analysis in Python using
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or NLP - Python proficiency and PyTorch experience - Familiarity with language models, Transformers, or generative AI - Ability to conduct independent research - Interest in trustworthy, explainable