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evaluating the efficiency and accuracy of both physics-based models and machine learning techniques, leveraging high-performance computing resources. The role will involve collaboration with leading
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machine learning to create improved reconstructions of the ice sheet. This role is based in Hobart, Tasmania and visa sponsorship and relocation allowances may be considered for the right candidate. What
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industry, research and civil society network. Its priority domains for public engagement are news and media, transport, social services and health. Learn more about the ADM+S Centre here: https
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@rmit.edu.au or visit our Careers page for more contact information - https://www.rmit.edu.au/careers. We are a Circle Back Initiative Employer – we commit to respond to every applicant.
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Conservation, at: elisabeth.strain@utas.edu.au. Please visit https://www.utas.edu.au/jobs/applying for our guide to applying and details on the recruitment process. For current UTAS staff, in submitting your
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in human induced pluripotent stem cells (hiPSCs)-derived skin organoids (https://pubmed.ncbi.nlm.nih.gov/38044307/ ), and the generation of vascularized skin organoids (https
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sustainable energy, environmental protection, medicinal and biomedical science, and advanced materials. Further details about the School can be found at: https://www.uwa.edu.au/schools/molecular-sciences About
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criteria from the Position Description below For further information about this position, please contact Professor Xiaolin Wang, Sciences and Engineering, Xiaolin.Wang@utas.edu.au Please visit https
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econometrics and machine learning, is highly desirable. You are well-organised and capable of managing multiple tasks to meet deadlines, with excellent written and verbal communication skills for producing clear
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of published research relevant to clinical medicine, especially those using quantitative research methodologies. Experience and demonstrated achievement in University-level teaching and learning. A record of