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, reproducible tools and datasets. • Infrastructure and benchmarking for large-scale social-science simulation and validated workflows. The group website is https://torrvision.com/ Feel free to add Professor
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Exciting and high-profile interdisciplinary research on visualisation, machine learning, and human-computer interaction Comprehensive computer infrastructure for AI and the analysis of large data volumes A
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
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of Helsinki. The main research fields at the department are artificial intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed
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: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological
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been revolutionized in recent years by machine learned interatomic potentials (MLIP), and questions that were impossible to tackle five years ago can now be addressed. The state-of-the-art approach
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Scene Understanding Detection and Identification of Objects (SSUDIO) project. The purpose of this project is to develop scene understanding from 3D scans of ships by applying machine learning/computer
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collection and employ relevant machine learning methods for data analysis and sensor fusion. The PhD Research Fellow will collaborate closely with another PhD Research Fellow at the Faculty of Health and