1,600 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "University of Warwick" positions at Nature Careers
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a team, along with strong social and communication skills Excellent command of English, both written and spoken For further information, please contact Silke Benndorf (+4930-83864398). Applications
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bioinformatics, computational biology, genomics, statistical genetics, or a related quantitative field, together with demonstrated expertise in large-scale genomic data analysis and significant experience in
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Aarhus University, and the place of work is Department of Biological and Chemical Engineering, Aabogade 40, 8200 Aarhus N., Denmark. Contacts Applicants seeking further information regarding the PhD
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and services by utilizing the computerized scheduling system in an accurate, efficient manner. Maintains scheduling (clinic-specific) information and computer knowledge to ensure safe and effective
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-EM sample preparation, data collection, and high-resolution structure determination • Process and refine complex cryo-EM datasets using strong computational skills (RELION, cryoSPARC, etc.) • Conduct
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information For further information, please contact: Professor Daniel Otzen, dao@inano.au.dk Application procedure Shortlisting is used. This means that after the deadline for applications – and with
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research and unite our researchers around our Technology Innovation Challenges and Life Science Challenges. For more information on the Franklin’s Challenges click here . As a Research Associate in Chemical
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Fabrication (DigiFAB) and the UKRI-funded AI for Chemistry Hub. Together, these initiatives provide exceptional opportunities for high-throughput chemical biology and medicinal chemistry, drug discovery, data
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Production, CP3: Assessing Crop Performance by Measurements and Phenotyping, CP4: Soil-Root-Interactions for Crop Performance, CP5: Sustainable Innovations in Cropping Systems, CP6: Fusing Information from
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perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g., active learning and other data-efficient approachesConduct large-scale