21 parallel-programming-"DIFFER"-"Humboldt-Stiftung-Foundation" Postdoctoral positions at University of Sydney in Australia
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. The ideal candidate will also be expected to have quantifiable experience with: A variety of different neuroimaging methodologies. Using multiple programming languages, including R, Python, MATLAB and
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Frontier for Science Program, and will involve collaboration with researchers from different disciplines across four countries. Your key responsibilities will be to: undertake research in metallomics and
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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
or a related discipline, with an emphasis in numerical computation intermediate to advanced skills in large data analysis and manipulation, including programming in different languages including Matlab
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stakeholders develop a coherent research program in translational metagenomics and an emerging research profile support and supervise junior researchers and clinical trainees contribute to the positive culture
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complex analysis an excellent track record of publishing high-quality papers in top-tier mathematics journals solid mathematical programming skills. Pre-employment checks Your employment is conditional upon
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, Psychology, or a related field. strong background in computational modelling and/or functional neuroimaging techniques. proficiency in programming languages commonly used in neuroscience research (e.g., Python
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to furthering that agenda contributes to the strategic direction of the Faculty/School/Portfolio as articulated through the Faculty/School/Portfolio’s Strategic Plan/s or equivalent undertake other duties and
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Ability to support the Professor’s research program Ability to assist supervising postgraduate students Ability to work independently and collaboratively Experience of presenting research results
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-scale, complex project environments. This project is part of an ambitious research program to integrate real-time sensing, AI-based control, and governance analytics into the future of infrastructure and
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of scientific monitoring programs demonstrated experience managing and integrating large datasets experience working in large teams and organising complex field programs a strong commitment to delivering outcomes