50 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" Postdoctoral positions at Aarhus University
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imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets
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/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning approaches, and development
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
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research profile within organisational studies, Computer-Supported Cooperative Work, Human-Computer Interaction or related research areas as documented by a PhD dissertation and/or research publications
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are expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The
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expected to: shine in individual and collaborative research, either to assist groups of bachelor’s students in doing homework or co-teach advanced courses relevant for your research area. The Department
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Science, please see here . Further information If you have questions regarding the position or want to learn more about the project and specific tasks prior to the application, please do not hesitate
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assistance and career counselling for accompanying partners. English is the primary language used for internal communication and teaching, and international candidates are not required to learn Danish. Aarhus
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learning for imaging tasks Prior work with histology–imaging registration or material decomposition Clinical research exposure As a person, you have good interpersonal skills, are inclusive and team-oriented
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. Furthermore, to be highly skilled with strong learning abilities and a positive mindset, it is expected that the candidate will lead all aspects of the project. A profound interest in both the methodological