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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's doctoral program (https://www.ntnu.edu/nv/phd) PLEASE NOTE: For detailed information
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, please visit: https://qbm.genzentrum.lmu.de/application/ Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
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, as well as other sustainability relevant endeavours Integrating advanced machine learning methods in thermodynamics for computer-aided property predictions, molecular and product design and
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at a computer for large portions of the day; repetitive motion; occasionally positioning patients over 25 lbs. Shift Monday – Friday, Day Shift; 7:30-6:00pm (40 hrs/wk), 4x 10-hour shifts Job Summary We
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focus on the dynamic nature of phase transitions in APIs, using machine learning interatomic potentials (MLIPs) to construct force fields whose mathematical complexity will be carefully controlled in
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University Medical Center of the Johannes Gutenberg University Mainz | Mainz, Rheinland Pfalz | Germany | 2 months ago
), is offering a fully funded PhD position in the area of statistical learning, machine learning, and survival analysis applied to large-scale proteomics and multi-omics cohort data. The PhD project
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(1–4) and in related projects. We encourage potential PhD candidates to visit our webpage to learn more about the research we are conducting. The PhD candidate is expected to be enrolled in two
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qualifications: Experience with implementation or applications of large machine learning models Experience with generative methods for protein design and/or docking simulations or generative methods
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with Machine Learning Highly motivated to learn about biology and (the study of) biological data Enthusiastic team player Desirable but not required Experience with single-cell omics data Experience with
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analysis. • Hydrological and hydraulic simulation. • Machine learning, including unsupervised clustering and predictive modelling. • Working with large, complex, multi-source datasets using MATLAB, Python