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and refined our pioneering AI-driven methods. This project focuses on improving protein structure prediction, design, quality assessment, and dynamics using innovative machine learning techniques. You
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processing (first, second and multilingual); multimodal language processing and learning; developmental language disorders; educational linguistics and language teaching, testing and assessment; and language
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science. You will be part of a dynamic research group with expertise in Earth Observation, geoinformatics, and machine learning, offering an excellent environment for advancing your research and building
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for Clean Energy Conversion: Learning Multiscale Dynamics in Fuel Cell Systems”. The project aims to develop a multiscale modeling framework that combines computational fluid dynamics (CFD), electrochemical
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, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and enrich the knowledge base (i.e. learning by
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semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can
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academic research, learning and outreach. We provide a competitive advantage by linking our top-level international and interdisciplinary academic performance in the areas of material science, nanotechnology
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us Stimulated by major needs and challenges in science and a sustainable society, the ambition of the Department of Physics is to foster a creative environment for academic research, learning and
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learning–based protein design, for the successful design of 2D lattices. These methods will then be applied to generate designs targeted for experimental evaluation. Work duties The main duties involved in a
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mathematics, data science and machine learning for image recognition. Moreover, you will develop methods and software that will allow new characterization of nanoscale materials. Therefore, your research will