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and other multimodal datasets Design and fine-tune machine learning and deep learning models to extract meaningful patterns and predict metastatic behavior Collaborate closely with experimentalists
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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: Research and extract industrial and commercial buildings across Germany using open-source geospatial data (e.g. OpenStreetMap, CORINE Land Cover, Sentinel, etc) Apply machine learning/AI
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, 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 and operational services by bridging
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, evolutionary histories, or domain architectures as potential drivers of altered bacterial virulence. You will apply supervised and unsupervised machine learning to correlate adhesin variation with virulence and
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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
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approaches are gaining importance for autonomous vehicles. However, the training and certification of autonomous systems with machine learning components is a huge challenge, since the learned behavior is
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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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Behavior Models (NBM), fault detection methods, and other operational analytics based on operational data from WTs. You will apply machine learning techniques and incorporate your understanding of wind
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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