338 parallel-processing-bioinformatics uni jobs at The University of Queensland in Australia
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in statistical analysis and machine learning techniques such as natural language processing, large language models, predictive modelling, network analysis, multivariate analysis and choice modelling
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process incoming and outgoing deliveries in the AIBN Goods Receiving Dock. The tasks will contain administrative duties. Key responsibilities will include: Provide laboratory support tasks to assist
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the subject line. Want to Apply? We welcome applications from all individuals and are committed to an inclusive and accessible recruitment process. To be considered, please ensure you upload: Cover letter
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, you'll support faculty and school staff, offering expert training and guidance on systems, processes, and student-related matters. You’ll also play a key advisory role, working closely with senior academic
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animal veterinary science. This will include veterinary medicine and surgery, herd health, husbandry and handling, nutrition, and reproduction for the major production animal species. As a teaching focused
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. Demonstrated ability to be reliable, punctual and ethical. Good verbal communication and time management skills. Computer literacy skills including the ability to send/receive emails, complete online forms and
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established team where multiple colleagues share the same types of responsibilities in a supportive environment. This position is responsible for completing a diverse range of administrative processing tasks
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interviews have been tentatively scheduled for 22 August 2025. UQ is committed to a fair, equitable and inclusive selection process, which recognises that some applicants may face additional barriers and
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applications from all individuals and are committed to an inclusive and accessible recruitment process. To be considered, please ensure you upload: Resume A cover letter summarising how your background aligns
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summarising how your background aligns to the ‘About You’ section Resume Other Information UQ is committed to a fair, equitable and inclusive selection process, which recognises that some applicants may face