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an ERC Advanced Grant and a Laureate Grant from the Novo Nordisk Foundation. We are now looking for 1-2 postdoc researchers with a PhD and/or MD (i.e. level of education corresponding to the Danish PhD
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program). Your qualifications A PhD in psychology or a related field. Knowledge of loneliness research, preferably with a focus on interventions. Experience with literature reviews and quantitative methods
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contribute to joint research efforts within the program. · Present your work at group meetings, conferences, and in high-quality scientific publications. · Contribute to the supervision of MSc and PhD students
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, a support-staff of ~30 people, ~150 PhD-students and postdocs and around 400 students. For further information, please contact Professor, dr. scient. et techn. Bo Brummerstedt Iversen (bo@chem.au.dk
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on understanding and controlling the structure of the casein micelle (CM), a key component in dairy systems, under various simulated processing conditions. The project will be caried out in close collaboration with
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Post Doctoral Researcher in Digital Twins CO2-to-Protein production in collaboration between the ...
collaboration between the Department of Electrical and Computer Engineering and the Novo Nordisk Foundation CO2 research center, Aarhus University, we aim to address this opportunity by developing digital twins
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about the Department at www.fysik.dtu.dk/english . If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark . Application procedure Your
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applications. Your profile Applicants should hold a PhD in Immunology, Microbiology, Neuroscience, or similar. Essential Experience and an international track record in neuroscience, microbiology, or immunology
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For further information, please contact: Assistant professor Rasmus Kock Flygaard, email: rkf@mbg.au.dk Deadline Applications must be received no later than 24th September 2025. Application procedure
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will work with data collected from the field to the spatial scale, and investigate spatial optimization approaches to improve the model parameterization at the spatial scale. We expect that you will be