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Field
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Models, Knowledge Graphs, and related fields (e.g., Graph Machine Learning) Tasks: scientific research in at least one of the following areas: Natural Language Processing, Knowledge Graphs, Machine
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experimental data. Develop computational frameworks for integrating spatial and bulk multi-omics datasets. Create and apply statistical and machine learning models for feature extraction, data harmonisation, and
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compiling information on a current topic in the field of machine learning. Researching and implementing novel machine learning and computer vision approaches. Self-critical evaluation of the obtained results
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remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance computing, and synchrotron-radiation
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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machine learning and deep learning methods will be developed and applied within interdisciplinary research networks. The professorship should also contribute to the generation and supervision
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) Focus on Microbiome Data Science and Explainable Machine Learning Core research themes We are looking for motivated and skilled students to join our research team in the field of plant microbiome data
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concepts and techniques of Artificial Intelligence (AI) and Machine Learning (ML) Knowledge in specific AI application areas such as Natural Language Processing (NLP) and Natural Language Generation (NLG) We
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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samples, phase retrieval in this regime remains challenging, limiting multiscale imaging approaches in near-field holotomography. To address this, the PhD project combines machine learning, high-performance