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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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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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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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Vision and Graphics, Statistical Learning, and Bioinformatics. Please visit the website at https://www.polyu.edu.hk/dsai/ for more information about DSAI. Duties The appointee will be required to: (a
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analysis, and basic feature engineering. Experience with Python or a similar programming language, and basic exposure to scientific computing or machine learning libraries, combined with an interest in
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students who are prepared for a lifetime of learning and rewarding work. Candidates should hold a PhD or master’s degree in electrical and computer engineering or related fields and should be comfortable
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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to state-of-the art AI/machine-learning, from modelling to measuring emissions and climate effects, noise and noise propagation. Job requirements The PhD position is funded by the Horizon-Europe TUNED
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data set (e.g. neutron irradiations, that take years/decades to generate). Digilab brings AI/ML (artificial intelligence / machine learning) approaches for data engineering and automation to utilise
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about the lab at: https://mbzuai.ac.ae/study/faculty/natasa-przulj/ and https://przulj-lab.github.io/ Qualifications PhD in Computer Science, Mathematics, Physics, Bioinformatics, or a related