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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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technologies generate unprecedented volumes of molecular data at cellular resolution, opening new avenues for the application of machine learning to fundamental biological problems. The postdoctoral researchers
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(HORIZON-INFRA-2024-TECH-01-01 – R&D for the next generation of scientific instrumentation, tools, methods, and solutions for RI upgrade), this position focuses on data curation activities under Work Package
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support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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existing structural and functional MRI data, acquire new data in collaboration with clinical researchers, and prepare publications and conference presentations. - Study preparation - Data acquisition (MRI
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic