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Informatics, Research Group Research-Development-Innovation is looking for a PhD-student with a doctoral grant. We invite applicants for a fully funded PhD position in machine learning, AI, and data science for
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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performance data using recommended guidelines and machine learning tools Defining the uncertainty sources Enhancing existing guidelines for full-scale power-speed assessment practice Disseminating research
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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]. 3. Explore the diversity of signals and perform complementary experiments to finalize the training dataset [Month 6 – 12]. 4. Develop a data analysis process based on machine learning for multimodal
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: an insight from Genetics, Single-Cell Transcriptomics, and Machine Learning. Profile of the graduate Ph.D. graduate has extensive knowledge of cell and developmental biology, ranging from basic principles
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | about 2 months ago
include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search
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the semantic foundation that enables AI systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI