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Jyvaskyla University of Manchester Kone Oyi The candidates will have the opportunity to visit various partners in the network, supported by a mobility allowance. At the Chair of Machine Learning for Complex
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Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning
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health, epidemiology, statistics, biostatistics, or machine learning/artificial intelligence. You must have a strong academic background from your previous studies and have an average grade from your
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research group (please find further information at https://www.biologie.uni-konstanz.de/gruber ). You will be developing machine learning-based data science approaches for the analysis of Next Generation
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profound knowledge in key areas of infection biology. At the same time, students learn to examine their research findings critically and to present them to an audience. In the last two half-year modules
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(s) Number of offers available1Company/InstituteDepartment of Computer ScienceCountryNorwayCityGjøvikPostal Code2815StreetTeknologivegen 22Geofield Contact City Gjøvik Website http://www.ntnu.no Street
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in C++ and/or Python is expected, and experience in model analysis and parameter optimisation is beneficial. Experience in machine learning and neural networks is desirable. The successful applicant
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
(as machine learning techniques, etc.). Personal characteristics In the evaluation of which candidate is best qualified for the PhD position, emphasis will be placed on education, experience and
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]. 3. Explore the diversity of signals and perform complementary experiments to finalize the training dataset [Month 6 – 12]. 4. Develop a data analysis process based on machine learning for multimodal
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the semantic foundation that enables AI systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI