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for this postdoctoral should have the following qualifications: Ph. D. degree in data science, electrical engineering, computer engineering, computer science, mathematical engineering, or similar. Proven track record in
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to contribute to a positive, creative and productive research culture in the iGRIDS group and the wider department. You must hold a PhD in Energy Engineering, Electrical Engineering, Mechanical Engineering with a
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techniques to monitor protein-protein interactions and prior knowledge of these is an advantage. Your profile The applicant must have a relevant PhD in structural biology or protein biochemistry. Who we
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have academic qualifications at PhD level, for example within areas of epidemiology, health sciences, medicine or related field. Strong skills within applied statistics, R programming and experiences
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QGG Aarhus University seeks two Postdoctoral researchers in Quantitative Genetics of sustainable ...
Master's and PhD students. Candidates will be responsible for creating a collaborative work environment within and outside QGG that integrates novel innovative research programs towards the green transition
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with an interest in ecological applications. Required qualifications: PhD (or equivalent) in computer science, biology, software engineering, or a related field Strong proficiency in Python, including
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range from cell biological over biochemical to molecular biology and bioinformatics approaches. Collaborations with structural biologists are possible. Your profile Applicants should hold a PhD in
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range from cell biological over biochemical to molecular biology and bioinformatics approaches. Collaborations with structural biologists are possible. Your profile Applicants should hold a PhD in
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Collaboration with computational protein design partners and clinical reproductive biology collaborators Supervision of MSc and BSc students and contribution to publications You must have: A PhD degree in
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of circumstances under which university scientific advice is given to private companies; A background within scientific computing, using tools such as R, Julia or Python; Knowledge of using data science in relation