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University of Stavanger invites applicants for a PhD Fellowship in in molecular modelling and machine learning for improved subsurface utilization, at the Faculty of Science and Technology, Department
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integration and optimized operation using machine learning and AI techniques as key drivers for improving system performance. The hired candidate will have the opportunity to work with cutting-edge energy
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calculations Knowledge about machine learning application in condensed matter Knowledge about magnetism, superconductivity, and topological order Personal characteristics We are looking for a candidate who is
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particle models, stochastic PDE and models from fluid dynamics and machine learning. What skills are important in this role? Qualification requirements: The Faculty of Mathematics and Natural Sciences has a
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viability data to discover new biomarkers and treatment strategies. You will work in a highly interdisciplinary environment spanning oncology, cell biology, imaging, bioinformatics and machine learning, with
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research and academic bodies. This collaboration is centered around a unique, open-source digital platform enriched with data and powered by domain knowledge-based advanced machine learning and artificial
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of Visual Intelligence is to develop novel, innovative solutions based on deep learning to extract knowledge from complex image data. Deep learning, aided by machine learning techniques in general, has led
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methods in forestry generate vast quantities of data and demand more accurate information. Machine learning allows for the systematization and processing of this data into new forms of information
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for forest applications Good presentation skills, written and oral Qualifications that will be emphasized Experience from research in boreal forest ecosystems Programming skills Experience with machine
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, geometric deep learning. Considered an advantage: experience in programming or course work in computer science, algebra, topology or differential geometry, knowledge of topological data analysis or machine