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, catalysis and/or surface science. For Topic 4, candidates must have documented skills within computational modelling of atomistic processes. Experience in scientific programming, e.g. using Python, is
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Measurements and Data Processing as per June 1st, 2026, or as soon as possible thereafter. The position is available for a period of 1 year, with the possibility of extension. In electronic engineering, Aalborg
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characterization of nucleic acids Computational model building and structure prediction Single-molecule fluorescence microscopy techniques Experience in cryo-EM structural biology of nucleic acids. Who we
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candidates will be involved in materials crystallography research in collaboration with other members of the Iversen group. The candidates must have a PhD in chemistry, crystallography, physics, materials
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prior teaching experience and the department’s need for teaching during the period of employment. Your qualifications Applicants are expected to hold or be close to completing a PhD in political science
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proteomics approaches are also encouraged to apply. Your profile Applicants should hold a PhD in proteomics, molecular biology, biochemistry, or a related field. We are particularly interested in candidates
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refinement. Publish results in peer-reviewed journals and present findings at international conferences Required Qualifications PhD in proteomics, analytical chemistry, physical chemistry, or a closely related
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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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