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Associate Data Scientist participates in biomedical research projects using programming, data -mining, statistics, machine learning, and visualization techniques to assist with the development, evaluation
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through applied research programmes. Faculty in the ICT Cluster undertake funded industry-relevant research, teach courses in Computer Science, Computer Engineering, Information Security and Software
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. The University of North Georgia is currently accepting applications for a Data Science Intern - Institutional Student Worker. This position will: Learn how to apply statistical and machine learning techniques
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well as predictive models based on machine-learning technologies, in order to carry out code development and testing activities within the listed projects; therefore, skills in software design and development
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- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute to a corpus of geo-analytical scenarios with
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Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP) | Paris La Defense, le de France | France | about 1 month ago
a postdoctoral researcher to work full-time on the DiscoReel project. The postdoc will work on developing machine and deep learning methods for epidemic modeling, integrating them with mechanistic
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at the interface of machine learning, statistics, and live-cell biology. The position is co-supervised by Prof. Olivier Pertz (Cell Biology) and Prof. David Ginsbourger (Statistics), and the student will be equally
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, or machine learning models). Experience with high-performance computing and version control (e.g., GitHub). History of large-scale project implementation work in an international setting (e.g, population
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proposals. Responsibilities Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. Lead and co-author manuscripts in statistical, machine learning
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reporting. Understand how data flows through EDW, ODS, and data marts. Learn fundamentals of dimensional modeling and data lineage. Develop precision, documentation habits, and professional communication