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and databases Artificial Intelligence for manufacturing Experience in all of the above topics is not expected Key Responsibilities The successful candidate will conduct high-quality research within
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requires research qualifications corresponding at least to what can be achieved through satisfactorily completed PhD degree within the subject area. The overall assessment of the applicant will emphasize
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Physiotherapy program, and to teach modules at other bachelor and master programs offered at the Faculty of Health Sciences as well as to mentor undergraduate and PhD students. Candidates with the following
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range of fields including applied cyber-physical systems, artificial intelligence, embedded computing, mechanical structure integrity and life assessment, heating and cooling, renewable energy systems
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QGG Aarhus University seeks a Tenure Track Assistant Professor in Quantitative Genetics of sustai...
information to improve genomic prediction models. The selected candidate will collaborate with industry partners, teach at undergraduate and graduate levels, and supervise Master's, PhD students, and Postdocs
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analytical chemistry. The ideal candidate holds a master or PhD degree and has extensive hands-on experience in assay development and SERS/Raman-based sensing as well as centrifugal microfluidics. You are
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, artificial intelligence (AI), machine learning, and computation have emerged as powerful digital technologies for creatively generating new design ideas and rapidly advancing formgiving methods within
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, including publication in high-impact scientific journals; be responsible for teaching, guidance and examination of BSc and MSc degree students and co-supervision of PhD students; as well as attracting
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Two Senior Innovation Coaches (specialkonsulent) for Early-stage Medical Technology Innovation Teams
BioMedical Design Novo Nordisk Foundation Fellowship Programme, Department of Clinical Medicine at the Faculty of Health, Aarhus University, invites applications for two positions (one in Aarhus and
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externalities of transport. The division is interdisciplinary with scholars originating from transportation engineering, economics, psychology, computer science, social data science, machine learning, mathematics