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to have equal qualifications, female applicants will be given priority. Application To apply for this position please follow the link "Apply for this job". Your application letter, relevant education and
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applications, while the candidate working on Deliverable 2 will apply theoretical physics and statistical methods to LISA data analysis. The work will be carried out under the supervision of Assoc. Prof. Germano
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learning models expansion of the application areas of such models through data set generation and testing of models In our group we have already made contributions in this field across a varied set of
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testing, instrumentation, data acquisition systems, and numerical or analytical modelling of fatigue damage will be considered an advantage. motivation and potential for research within the field
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, assess structural responses, and identify damage under extreme conditions. By combining computational simulations with data-driven approaches, the project will provide practical models for hazard
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journals with ongoing data collection and possibility of further expansion. The candidate will work at the intersection of AI, information retrieval, multimodal learning, and clinical decision-making
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of these categories, feel free to indicate it when applying for the position. Learn more about the criteria for being considered an applicant in these specific groups here . Contact information More information on the
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participatory ethnography, and comparative case studies. You must be willing to work closely with scholars across methodological divides, and to analyse text data through quantitative and qualitative methods
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education. Research topic The PhD Fellow will be affiliated with the research project “Resuscitation data analysis”, which is a collaboration between researchers at Department of Mathematics and Physics and
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information More information on the position (and project description) can be obtained from Head of department Silje Haus-Reve, tel: +4751832782, e-mail: silje.haus-reve@uis.no or project leader Olga Gjerald