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of statistical software like STATA and SPSS for data cleaning, analysis, and interpretation, as well as R for data analysis and visualization in order to conduct qualitative research focused on gynecologic
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Master's and PhD students. Candidates will be responsible for creating a collaborative work environment within and outside QGG that integrates novel innovative research programs towards the green transition
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: office space, computer equipment, access to institutional digital resources, and the possibility to acquire pedagogical and scientific materials (books, software, licenses, etc.). The recruited candidate
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 24 days ago
machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern thinking, connecting decades of computer science methodologies with modern data and
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funded by a EU programme Reference Number BAP-2026-180 Is the Job related to staff position within a Research Infrastructure? No Offer Description In IT speak, a bug is an unintended (software) error. A
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methodological research interests, and could contribute to delivery of Masters level teaching, and supervising postgraduate research students. You should have a Masters and PhD, or equivalent work experience, in
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Force Microscopy. Electroactive biomaterial experience, including electrochemical characterisation and synthesis. Expertise with advanced graphing and/or data analysis software (Prism, Origin Pro, Matlab
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. · Ability to perform blood flow analysis with a software. · Clinical experience in cardiac and aortic surgery. Columbia University is an Equal Opportunity Employer / Disability / Veteran Pay
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and production of software tools for in silico screening of chemical libraries and rational drug design of molecules for therapeutic purposes. - Contribute to the development of a high-throughput
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Enhancement of AI/ML with in-network computing & processing Adaptation & optimization of AI/ML software libraries for non-conventional hardware architectures Physics-informed ML surrogates for efficient