114 machine-learning "https:" "https:" "https:" "https:" "https:" scholarships in Germany
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Your Job: Working with a broad range of imaging modalities (e.g. structural, diffusion-weighted and functional MRI, intracranial EEG) Multi-scale modelling of human brain development Using machine
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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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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Freiberg, Sachsen | Germany | about 1 month ago
separation models to control flotation processes. Your tasks # Develop and implement soft‑sensor concepts for continuous monitoring of ore microstructures along the entire process chain using machine‑learning
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environment of the transregio, one of the leading thoughts is the implementation of the general ''Problem-Based Learning'' principle, i.e., to combine the studies with hands on experience in research wherever
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simulation environments, numerical methods, or machine learning approaches is an advantage Fluent command of written and spoken English is necessary; German is an advantage but not required High degree
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microstructures along the entire process chain using machine‑learning (ML) techniques and validate soft‑sensor outputs against laboratory reference measurements Perform systematic laboratory flotation experiments
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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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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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) 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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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning-assisted PSPR optimization of recently developed lean Mg-0.1 Ca alloy