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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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analysis Large language models or machine learning/predictive modeling for longitudinal data analysis Strong computer programming skills Strong mathematical or statistical skills Ability to work as a part of
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models, and ensuring students have a structured and engaging learning experience. Career Readiness Competencies: Access & Opportunity Leadership Professionalism Essential Functions Teach assigned courses
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Machine Learning - Developing Oral and written communications - Developing Programming Languages - Developing The core technical skills listed are most essential; additional technical skills may be required
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 10 days ago
was installed at IT4Innovations in 2025. For more details, see www.it4i.eu . Activity description: · modelling and optimization of electrical networks using open source tools (preferably Julia or Python
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model
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environment where machine learning meets real-world scientific impact. What You’ll Do: Conduct cutting-edge research at the intersection of AI and science Develop large-scale deep learning models for scientific
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Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling, or machine-learning frameworks is an asset Strong analytical skills with a solid
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Machine Learning (ML) models, including automated testing, reproducible builds, controlled release strategies, and governance workflow integration. Build and manage containerized infrastructure on AKS
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intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in