48 software-verification-computer-science positions at Linköping University in Sweden
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learning applied to materials science Experience in large-scale computing and/or organizing large datasets in materials science Proven ability to independently author scientific publications Excellent
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application! We are looking for a PhD student in Medical Science. Your work assignments As a PhD student, you will participate in the project: Predictive markers for chemotherapy-induced toxicity in childhood
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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 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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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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Science for Sustainability (WISE ). The WISE Fellow recruitment package includes salary for the recruited faculty member (4 years full-time) salary for two PhD students (4 years each) salary for two
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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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We are now looking for a new Postdoc in Organic eletro chemistry that will join us at the Laboratory of Organic Electronics (LOE), a division of the Department of Science and Technology
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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