29 algorithm-development-"Prof"-"Prof" Postdoctoral positions at Aarhus University in Denmark
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contribute to development of research grant applications. Your profile The applicants should hold a PhD in structural dynamics with focus on data-driven methods (e.g., for input/state/parameter estimation) and
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of the position include: Pursue visiondriven, world-class research as a team that spans across broad linguistic, technical and historical competencies Working in a team that develops new methods in the intersection
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an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all
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Faculty of Natural Sciences, and Aarhus University Hospital. The overall project will leverage an existing clinical cohort, as well as develop the framework for a new cohort of patients with multiorgan
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qualifications: A solid background in programming using Python, R, or other languages. Teaching and supervision experience at the BSc and MSc level. Interest and experience in developing competitive national and
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contributes to developing the academic field in question. Your qualifications To qualify for the positions, you must have been awarded the degree of PhD or have acquired equivalent qualifications. Who we
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MSc level Interest and preferably experience in developing competitive national and international research applications Experience in managing large datasets and modelling Experience in planning and
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, or landscape modelling Further, we will prefer candidates with some of the following qualifications: Teaching and supervision experience at the BSc and MSc level Interest and preferably experience in developing
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research. Furthermore, this grant aims to significantly expand our understanding of the use of Large Language Models in Agile software development and is to be carried out in collaboration with a large
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engaging research environment. The postdocs will work on cultural alignment and preference optimization of large language models (LLMs) for mid to low-resourced languages. The overarching goal is to develop