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Field
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PhD (or equivalent) in an appropriate discipline. Have the potential for leadership qualities in the subject, as illustrated, for example, through showing initiative on research projects. Have an
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, or be near completion of, a PhD (or equivalent) in an appropriate discipline. Have the potential for leadership qualities in the subject, as illustrated, for example, through showing initiative
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. It is a prerequisite that you complete the project during the employment period. Qualification requirements You must have a PhD with a specialisation relevant for the project. The PhD thesis must have
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PhD Research Fellowships: Artificial Intelligence Adoption, Sustainable Finance, and Twin Transition
: Professor Atle Hauge, phone number: +47 97 08 48 86, e-mail: atle.hauge@inn.no Head of PhD programme Marit Engen, phone number: +47 61 28 81 07, e-mail: marit.engen@inn.no How to apply Please indicate in your
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for three years with research duties exclusively, or four years with both research and other career-promoting work. This will be clarified in the recruitment process. Research topics One PhD fellowship
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mark possible), research papers rated 4*, impact rated 4*, environment rated 4*(joint), Grade Point Average and Power. REF2021 rated as world-leading: 100% of our environment, 86% of our impact and 70
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mosquito vector. This is to dissect the protein network and how key novel signalling proteins are involved parasite proliferation stages. The work will involves gene targeting both conditional knockdown and
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(e.g., shopping malls). Located at the intersection of machine learning, robotics, and acoustic signal processing, the project will bring together a highly interdisciplinary team of researchers, industry
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) The University of Strathclyde has a significant depth of space research expertise across its Applied Space Technology Laboratory, the Sensor Signal Processing & Security Laboratory, and the Aerospace Centre
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of excellence. The main focus will be on developing novel voice and language-based natural language processing (NLP) methods for languages with less data than ideal for the development of cognitive tests