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: Artificial intelligence (Machine learning, Deep learning) Planned duration employment 12 months. Expected date of employment 2nd quarter 2025 Where to apply E-mail paulina.liszkowicz@uj.edu.pl Requirements
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the interfacial transition zone, - adjustment of rheological properties through tuning of concentrations and types of viscosity modifiers and superplasticizers, - deep learning modeling
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properties through tuning of concentrations and types of viscosity modifiers and superplasticizers, deep learning modeling of parameters of cement composites. The project is realized at the Bydgoszcz
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around 8 850 PLN per month pre-tax. The position comes with no teaching obligations and with a travel budget. Selected candidate will work on developing self-supervised learning methods and training a deep
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- using deep learning methods and artificial intelligence libraris like Keras, TensorFlow and Scikit-learn - provide web-based applications of prediction tools using Streamlit or similar and basic
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of programming languages (Java, Kotlin, Python) and collaborative tools (e.g. GitHub), 5) knowledge of basic algorithms used in machine learning and deep learning, with particular emphasis on image analysis
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; Development of models to describe forest condition based on environmental data and stand parameters using various statistical/machine learning methods; involvement in dissemination activities; publication
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cutting-edge design tools, including deep learning methods, to craft and evaluate synthetic nanocage structures, Study the 3-D arrangement of specific protein binders on a designed synthetic cage, delving
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algorithms (e.g., in quantum machine learning methods) and to develop new ones with promising practical applications. Furthermore, we intend to apply these mathematical insights to symmetry-based reductions