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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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, United States of America [map ] Subject Area: Computational Science / Artificial Intelligence/Machine Learning Appl Deadline: (posted 2025/11/19, listed until 2026/01/26) Position Description: Apply Position Description
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machine learning methods and applications focusing on the aspects outlined above, inspired by the needs of societally relevant applications, e.g. from neuro- or plant sciences. Advance our understanding
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membrane performance. Therefore, this objective of this research is develop efficient algorithms and models based on deep learning to accelerate the physics simulation for membrane relevant processes, which
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methods for predicting the synthesisability of organic molecules Develop and apply informed machine learning methods to chemical problems If appointed at a higher spine point: To assist in the day-to-day
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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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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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Machine Learning - Open Rank Faculty Mohamed bin Zayed University of Artificial Intelligence: Academic Appointments: School of Computing: Machine Learning Location Abu Dhabi Open Date Oct 21, 2025
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improving plant health using machine learning and artificial intelligence. Mentor(s): The mentor for this opportunity is Yulin Jia (yulin.jia@usda.gov ). If you have questions about the nature of the research
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approaches for using machine learning to analyze X-ray data, particularly Resonant Inelastic X-ray Scattering (RIXS). The position will collaborate with experts in RIXS experiments (Mark Dean), computational