877 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Univ" positions at Nature Careers
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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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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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through collaborative participation, supporting team operations and contributing to internal service roles. These are research focused positions. Further information can be found by viewing UQ’s Criteria
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of evolutionary anthropology and medicine (W2) to be filled at the earliest possible date. The professorship will represent the field of population genetics in both research and teaching. Its focus is on the data
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50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and understanding by employing staff who bring unique perspectives to our department
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Biomedical Engineering, Electrical Engineering, Computer Science, Data Science, Biomedical Informatics (Computational) or a related discipline who possesses strong computational and analytical expertise
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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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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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panel, shape the selection of strategic alternatives. Most of his research uses large-scale quantitative data analysis alongside semi-structured interviews. He uses unique data that he collects by
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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