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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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: 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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the robot's physical embodiment suffer from poor generalization, weak explainability, and limited transferability; (ii) sample-inefficient learning requires large volumes of annotated, domain-specific data; and
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Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological
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Engineering and Mechatronics. The Proposed PhD thesis topic: “Intelligent Diagnostics of Electrical Machines through AI-Enabled IoT Systems: Design of Custom Embedded Hardware and Protocol-Aware Architectures