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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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; • characterization of quantum correlations, entanglement, nonlocality; • machine learning for optimization problems. Website for additional job details https://mfi.ug.edu.pl/strona/112497/konkurs-na-stanowisko-post
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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
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Profile Leading Researcher (R4) Positions PhD Positions Country Poland Application Deadline 29 Dec 2025 - 00:00 (Europe/Brussels) Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer
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the necessary interface between the computer and the neural network of the human retina in vivo – without introducing additional modifications to our organisms. Such an interface could, in turn, be developed
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is modelling and optimization using molecular dynamics and machine learning. The position is part of a team looking at these fascinating systems from their fundamental properties and making them in
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Big Data, machine learning, and artificial intelligence would be an additional advantage; • knowledge of the basics of statistical model construction; • teaching experience in teaching at a university
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problems in partial differential equations inspired by cutting-edge algorithms in statistics and machine learning Type of programme/project/ undertaking MAESTRO-16 Funding institution National Science Centre
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, experience in conducting research in the area of Artificial Intelligence and machine learning, recent scientific achievements in the discipline of computer science indexed in Scopus or Web of Science
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. Specific Requirements PhD student status at least at the day of signing the contract in accordance with the project assumptions, theoretical and practical experience in reinforcement learning models (RL