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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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• experience in working with a synthesizer (knowledge of phosphoramidite chemistry preferred) • ability to prepare the text of a scientific publication and present the results • knowledge of computer programs
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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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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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development) including robotics, computer vision, natural language processing, information organization and retrieval, biomedical application and machine learning. Author of at least 2 research papers in topics
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methods and potential application of machine learning techniques. Good command of English (B2/C1). The candidate will be responsible for carrying out tasks related to the analysis of experimental data
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. Essential qualifications: PhD in Computer Science, Robotics, Machine Learning, or related fields. Programming skills in Python/C++ and experience with ML frameworks (e.g. PyTorch, TensorFlow, JAX). Strong
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of data at high luminosity (after Upgrade-2), participation in coordination of activities in software groups or data analysis groups, coaching of PhD as an auxiliary supervisor on machiner learning, new
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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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present the results • knowledge of computer programs such as Origin, Mendeley, MNova Specific Requirements The competition is open to individuals who meet the requirements specified in Article 113