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for large-scale data analysis, complex simulations, and the development of next-generation artificial intelligence and machine learning models. The responsibilities will include: • Developing and teaching
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning
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, or similar) will be valued; 9) Experience in machine learning techniques applied to materials science or process engineering (regression, classification, optimization, predictive models) will be valued; 10
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to significantly extend our existing team’s capabilities for data scoring and analysis (e.g., with expertise in natural language processing, machine learning, or computational modeling). Finally, the
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teaching faculty to teach an undergraduate course, Machines that Create, an introductory yet comprehensive overview on Generative AI and Foundation Models, covering the methods and techniques driving modern
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tools in research. Excellent written and oral communication skills, with a proven track record of publishing scientific papers and delivering presentations. Experience with machine learning techniques
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beneficial: Working knowledge of statistics and usage of MATLAB or other software for statistical analysis; Experience with machine learning and data mining. Good Estonian language skills Application procedure
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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attending an academic Bachelor’s degree in the scientific field mentioned above. Knowledge or experience (preferred) on machine learning or computer vision techniques, and interest in developing such skills
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or Python to analyze data and experience with statistical, machine learning, and data science approaches. Prior experience working in teams on collaborative projects. Knowledge, Skills and Abilities