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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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in a field related to one of the three research areas of MCML: Foundations of Machine Learning; Perception, Vision, and NLP; and Domain-Specific Machine Learning. The Munich Center for Machine
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posts,PHD Thesis Starting date: 30.10.2025 Job description: DESY Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of
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Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning
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Bayesian machine learning to improve risk management for bridge portfolios. We offer a funded PhD position in an excellent research environment. The project Our infrastructure is aging, and decisions about
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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on the ERC project Learning Matters!. Task You will implement learning mechanics in soft matter, specifically in biocompatible hydrogels. Your hydrogels will form the walls of a synthetic vein network that can