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Engineering; knowledge of English at a level sufficient to teach foreign students; documented experience in managing research projects; publications in scientific journals with a high Impact Factor; experience
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researcher with a strong scientific record (experience in leading research projects and completed postdoctoral fellowships is welcome), who is ready to teach courses in physics or computer science
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competence at minimum B1 level, • computer literacy (MS Word, MS PowerPoint, MS Outlook, MS Excel), • experience in LDL and/or knowledge of MS Teams, Zoom, Clickmeeting, etc, • substantiated own teaching
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, • computer literacy (MS Word, MS PowerPoint, MS Outlook, MS Excel), • experience in LDL and/or knowledge of MS Teams, Zoom, Clickmeeting, etc, • substantiated own teaching materials customized for teaching ESP
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, • computer literacy (MS Word, MS PowerPoint, MS Outlook, MS Excel), • experience in LDL and/or knowledge of MS Teams, Zoom, Clickmeeting, etc, • substantiated own teaching materials customized for teaching ESP
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the project is the development of AI-based pipelines for detecting, segmenting, and classifying lichen communities. Convolutional neural networks (e.g., U-Net, DeepLab) and machine-learning algorithms (e.g
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of biomedical signal recognition, with particular emphasis on brain data, and concerning machine learning, Research experience in the areas of artificial intelligence, machine learning, and biomedical signal
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++; knowledge of machine learning, deep learning, neural networks; LanguagesPOLISHLevelExcellent Additional Information Selection process Competition application should include a letter of application to
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of Mathematics and Computer ScienceCountryPolandState/ProvinceMalopolskaCityKrakowGeofield Contact City Kraków Website http://www.matinf.uj.edu.pl/ Street ul. Łojasiewicza 6 Postal Code 30-348 STATUS: EXPIRED X
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spectroscopy (e.g. CD, FTIR, NIR), fluorescence techniques (e.g. fluorescence anisotropy, time-resolved fluorescence, fluorescence lifetime imaging), HPLC chromatography and the application of machine learning