41 computer-science-programming-languages-"The-University-of-Akureyri" positions at Linköping University
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application! We are looking for a PhD student in Computer Science formally based at the Department of Computer and Information Science (IDA) as part of the national research program WASP. Wallenberg AI
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writing and speech. A solid background in software tools and engineering, operating systems, compilers, concurrent programming and in programming distributed, parallel and heterogeneous computer systems is
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written English. It is also advantageous if you are currently studying at Linköping University. The workplace The Department of Computer and Information Science was founded in 1983 but its roots go back to
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connection with your admission to the doctoral program, your employment as a PhD student is handled. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping
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of full-time. Your qualifications You have graduated at the Master’s level in Molecular Biology, Computational Biology, Immunology, or a related field, or completed courses with a minimum of 240 credits
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and first cycle studies specialising in environmental science are carried out at the department. First cycle studies include a bachelor's and a master's programme, as well as stand-alone courses and
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and advanced levels, primarily in our engineering program in Construction Engineering and our master's program in Digitalized Construction. Course orientations where you may be involved include
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undergraduate and advanced levels, primarily in our engineering program in Construction Engineering and our master's program in Digitalized Construction. Course orientations where you may be involved include
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AI, Autonomous Systems and Software Program (WASP). As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description