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high-impact publications. 2-year appointment, with potential for extension subject to performance and fund availability. Responsibilities Develop advanced statistical and machine learning modeling
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’. The role-holder will work closely with medicinal chemists at University of Oxford and pharmacologists at University of Glasgow, applying virtual screening, machine learning, AI-driven generative chemistry
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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About the Opportunity About the Institute Do you want to be part of an exciting new Institute focused on combining human and machine intelligence into working AI solutions? We are launching a
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. Experience with data-driven modelling, machine learning, or AI applications in energy systems is an advantage. Familiarity with modelling of energy networks, district cooling systems, or integrated urban
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machine learning techniques to develop emulators for the theoretical predictions of various observables as function of cosmological parameters. The candidate will develop and use skills in topics such as
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recently funded centre of excellence (Integreat). Integreat collects scientists from statistics and computer science and offers a flourishing machine learning community, including many PhDs and PostDocs
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samples. Apply machine learning and deep learning techniques to automate segmentation and quantitative analysis of tomographic refractive-index data from cells and tissue samples. Apply the developed
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 months ago
machine-learning methods to investigate the deep-time controls on copper mineralisation. The role will involve developing reproducible computational workflows, generating predictive maps of copper
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Species Spread Faculty Mentors: Dr. Tih-Fen Ting (Ecology, Environmental Science), Dr. Yanhui Guo (AI, Computer Vision), and Dr. Yun Zhao (Remote Sensing, Drone-Based Environmental Monitoring) Focus