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biological matter using X-ray and neutron scattering. The main research areas are materials for photovoltaics, proteins in solutions and at the interfaces, complex nano-structured materials and machine
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | about 2 months ago
and strategies. We recently developed machine learning tools to recover plasmids from metagenomic assemblies and characterized their ecology and evolution in the human gut (https://www.nature.com
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with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine learning, and porous materials strong analytical and
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) simulations will also be performed for the investigations. Furthermore, machine learning can be tested to accelerate MD simulations. In this project, you will be responsible for the following tasks in
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localization). This work focuses on machine learning-assisted PSPR optimization of recently developed lean Mg-0.1 Ca alloy produced by PBF-LB. After identification of the most relevant parameters adopting a
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and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics
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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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Your Job: In this position, you will be an active member of our "Simulation and Data Lab for AI and Machine Learning in Remote Sensing", which aims to strengthen interdisciplinary research by
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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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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative