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Salary: £21,653 - £22,922 per annum pro rata of £35,608 - £37,694 per annum Contractual hours: 22.5 Basis: Part Time Package: Full details can be found here - https://www.ucb.ac.uk/about-us/work-for
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Salary: £23,715 - £24,259 per annum pro rata of £26,707 - £27,319 per annum Contractual hours: 37 Basis: Full Time Term Time Package: Full details can be found here - https://www.ucb.ac.uk/about-us
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 21 hours ago
Position Details Position Information Position Title Postdoctoral Fellow – Aging and Care in the Context of Extreme Events Posting Number PTAP3537P Department/Unit Social Work Location Halifax, Nova
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. The PhD student is expected to develop and apply methods for causal inference will be part of the SMARTbiomed Pioneer centre https://SMARTbiomed.dk/about-SMARTbiomed. Project Description The PhD project
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funding for fieldwork and conferences, and room to develop your own research profile. Read more about being an employee at Lund University: https://www.lu.se/en/about-university/work-us Work duties and
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the technical characteristics and business context of data. Together, you help translate reporting, analytical and operational needs into sustainable data solutions rather than solutions built around individual
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for online and in-person courses. For more information about our department and programs, please visit: https://ritchieschool.du.edu/academics-education/departments/computer-science Position Summary Adjunct
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About us: King’s Digital Lab (KDL - https://kdl.kcl.ac.uk/) is a Research Software Engineering (RSE) team embedded within the Faculty of Arts and Humanities at King’s College London. We design and
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-focused research within agricultural or environmental contexts. In addition to having a background in the function, productivity, health, and sustainability of managed soil systems, successful applicants
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are seeking a PhD candidate to join our young team (Computational Systems Medicine) within the context of the BMFTR-funded DIASyM (https://diasym.mscoresys.de/ ) project, to develop new statistical and