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Field
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), physics-informed generative modeling and representation learning, domain adaptation, ML-assisted lattice field theory, and quantum algorithms and Hamiltonian simulation for high-energy physics, etc
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research focused on biomedical image computing. Our work involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim
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, optimize the control algorithms and compare the results. The candidate should be able to develop software programs (patient simulator and graphical user interface using Matlab), to test and validate
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, optimize the control algorithms and compare the results. The candidate should be able to develop software programs (patient simulator and graphical user interface using Matlab), to test and validate
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samples. All computational methods and algorithms will be implemented as part of the python based MetaboLabPy platform (https://doi.org/10.3390/metabo15010048 , https://github.com/ludwigc/metabolabpy
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of civil protection assets; - Researching and developing possible ways of calculating the risk of exposure of civil protection assets; - Applying machine learning algorithms to assist in risk calculation
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currently deposited at Zenodo. We will re-annotate the proteins to comprehend the underpinnings of fungal ecology and adaptability. The project will integrate information on the evolution of Mortierellomycota
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collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS focuses on the development of intelligent algorithms designed to improve the performance
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this project, you will develop innovative quantum algorithms for the accurate calculation of materials properties. You will combine methods from quantum informatics and solid-state physics to describe
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creation of a database for the various pollution sensors with a view to training online (non-embedded) models in the first instance. - Development of a machine learning algorithm based on the study database