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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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(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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representatives o carrying out data analysis and report writing. Candidates will have (or be working towards) a postgraduate degree (ideally PhD) in any of the sectors: Data Science / AI Machine Learning
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different backgrounds. This position requires that you have graduated at Master’s level in in computer science, media technology, computer engineering, human-computer interaction, visual learning and
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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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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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analysis. • Hydrological and hydraulic simulation. • Machine learning, including unsupervised clustering and predictive modelling. • Working with large, complex, multi-source datasets using MATLAB, Python
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-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
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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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The Applied Mathematics and Computational Sciences (AMCS) program in the Computer, Electrical and Mathematical Sciences and Engineering Division (https://cemse.kaust.edu.sa) at King Abdullah