108 coding-"https:"-"Prof"-"FEMTO-ST" "https:" "https:" "https:" "https:" "University of Groningen" Fellowship positions in Australia
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Strait Islander people, and people of all abilities, cultures, sexual orientation, and genders. Pre-employment safety and suitability checks: In accordance with the National Higher Education Code
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experience in using statistical and mathematical tools to analyse and interpret soil data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python
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in resistance management. Recording, managing and analysing data using established data science methodologies (FAIR, coding workflows, version control). Preparing publications, reports and conference
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to support translational outcomes and shared priorities perform quantitative and qualitative data collection, coding, analysis and interpretation, and contribute to the production of high‑quality peer‑reviewed
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citizenship behaviours align with the ARC-IFC’s Code of Conduct and UQ values. Build and maintain strong relationships with Indigenous communities and ARC-IFC’s community partners and other end-users
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funding Knowledge on a coding language (Phyton, etc) The successful candidate may be required to complete a number of pre-employment checks, including: right to work in Australia, conduct concerning gender
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and optimise code and digital-twinning platforms with industry partners, produce high-quality research outputs and reports, contribute to funding applications, and deliver training workshops and
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(e.g., LabArchives, BioRender). Desirable Demonstrated experience in fermentation control, optimisation and scale-up. Experience in the use of electronic lab books Experience in coding languages (Python
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experimental tasks using Gorilla or other programs that can be used for online tasks. Align with and actively demonstrate the Code of Conduct and Values. Cooperate with all health and safety policies and
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complete Data management for the project including data collection, cleaning, coding, analysis and interpretation of primary/secondary datasets using statistical software packages collaborate and build a