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Sciences Salary: Level B - $119,231 - $141,581 p.a. plus 17% super Contribute to transformative research in agricultural innovation, digital digital and agricultural extension and advisory systems
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for an outstanding academic with a passion for social science, agricultural extension and digital irrigation to fill a new position in our Rural Innovation Research Group. In this role you will work on a large
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Sciences Salary: Level B - $119,231 - $141,581 p.a. plus 17% super Contribute to agricultural research that benefits farmers and the community Apply now to lead research in digital irrigation, facilitating
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mathematics or a related discipline, combined with experience in microbial culturing techniques and imaging/microscopy. With a growing publication track record and a clear ambition to build a research career
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mathematics or a related discipline, combined with experience in microbial culturing techniques and imaging/microscopy. With a growing publication track record and a clear ambition to build a research career
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backgrounds. You also have: The candidate must hold a PhD A disciplinary background in the Social Studies of Technology and/or Anthropology and/or Digital Media Studies and/or Human-Computer Interaction, or
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evaluations of pharmaceuticals, health services and technologies, preventive and curative programs, and digital health applications. Be Yourself The University of Melbourne is an Equal Opportunity Employer and
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the forefront of computing research in Australia and internationally with close links to major computing research initiatives, including Melbourne Bioinformatics, CSL, The Cremorne Digital Hub and CSIRO’s DATA61
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Melbourne Bioinformatics, CSL, The Cremorne Digital Hub and CSIRO’s DATA61. The School’s aim is to attract and retain outstanding staff in order to maintain a leading research and teaching profile. We have an
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related discipline with significant relevant experience. Have extensive experience with biological mass spectrometry techniques and chromatography. Possess strong skills in handling large data processing