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evaluation process etc please visit: ambercofund.eu Qualification requirements Minimum requirements are: candidate needs to have a maximum of 8 years after a doctoral degree (PhD), as required by the project
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profiling with cutting-edge in vivo physiological phenotyping and pharmacological engineering. Through this comprehensive approach, we believe we are poised to make transformative breakthroughs in
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evaluation process etc please visit: ambercofund.eu Qualification requirements Minimum requirements are: candidate needs to have a maximum of 8 years after a doctoral degree (PhD), as required by the project
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the section Ingredient and Dairy Technology. Further information on the Department is linked at https://food.ku.dk/english/ . Inquiries about the position can be made to Tenure Track Assistant Professor Norbert
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cooperate on teaching Your profile We are searching for a highly motivated candidate who has: A PhD in agronomy, engineering, biology, technical oriented sciences or similar Research experience on process
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independently Excellent English and basic knowledge of the Danish language As a formal qualification, you must hold a PhD degree (or equivalent) in environmental engineering or a similar scientific area. We offer
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shared with about 400 highly skilled and motivated colleagues, with interdisciplinary backgrounds from meteorology, electrical and control engineering and data science all the way to humanities
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Lonza’s expertise and technology within peptide T cell immunogenicity, and the vast expertise within immunoinformatics and machine learning models at DTU to address this challenge. This will enable
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Postdoc in Psychiatric Epidemiology: Linking Register and Trial Data to Study Postpartum Depressi...
closely with a PhD student and the broader project team and will contribute to the implementation and follow-up of the randomized controlled trial evaluating internet-based therapy for mothers with
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will benefit from Lonza’s expertise and technology within peptide T cell immunogenicity, and the vast expertise within immunoinformatics and machine learning models at DTU to address this challenge