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of Denmark and in Greenland, and we collaborate with the best universities around the world. Job Info Job Identification 5453 Job Category TAP Posting Date 07/02/2025, 11:26 AM Apply Before 08/11/2025, 10:59
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. Responsibilities and qualifications Qualifications: PhD degree in Engineering, Physics, Computer Science, or Applied Mathematics. Proficiency in scientific programming with Python. Excellent oral and written
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submitted your application, you must send this person’s details (name, job title, place of work, and email address) as well as the name of the position you have applied for to: HR.Nattech@au.dk Formalities
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Genetic Improvement of Dairy Cow Longevity, Using Large-Scale Body Weight Data from an AI-Camera ...
Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is
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Scientist in the field of MEG and Cognitive Neuroscience, starting September 1, 2025, or as soon as possible thereafter. The position is a fixed-term full-time post for 2 years, with a possibility of
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performance computing) services. We are looking for a new colleague for a newly established position, someone with insight into and interest in the technical aspects of FAIR data management solutions and
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and code. Competences and Skills: • Experience in MLOps, cloud/edge computing, software architectures, data engineering/ML tooling, and reinforcement learning. • Good collaboration capabilities
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have to: Participate in formal pedagogical training program for assistant professors. The associate professor’s additional responsibilities will primarily consist of: Research leadership, including
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for forensics, life sciences, topological data analysis, spatial statistics, and computational statistics). We encourage all qualified researchers to apply, especially those with research experience or future
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in teams of scientists and support staff with competences spanning a wide range of fields including applied cyber-physical systems, artificial intelligence, embedded computing, mechanical structure