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: algorithmics, graph transformation and algorithm engineering. Exposure to systems chemistry or systems biology is an asset but not a must. Proven competences in programming and ease with formal thinking are a
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increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection
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project (thesis, paper, etc.) Analytical mindset: You have a passion for solving complex problems and a solid foundation in math/stats/ML theory Teamwork and Communication: You enjoy interdisciplinary
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). Maintenance of transgenic mouse colony and embryonic fetal analysis 3). Train students and post-docs in hematopoietic research techniques 4). Prepare figures. Make charts, tables, and graphs from numeric and
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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demonstrated experimental realizations and proven theoretical advantages. The project may involve several aspects, including mathematical theory, algorithm development, error correction, adaptation of GBS-based
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biological data, development of deep learning and large language models for biological discovery or graph-based methods for molecular and cellular networks. The technological foundation further consists
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Wikibase instance Curate and model historical migration datasets within the dedicated Wikibase instance Contribute to the design of ontologies and metadata schemas for the knowledge graph Develop data-driven
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, and theoretical analysis using Leslie-Ericksen theory for liquid crystal flow as well as theory for viscoelasticity of polymeric liquids. Both in the practical and simulation/theory work you will be
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large-scale MD simulations, ideally with LAMMPS, demonstrated via corresponding roles in publications Experience with density functional theory (DFT) calculations, ideally with VASP, demonstrated via