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• Participate in the department’s research environment • Complete a PhD training programme • Teach at one or more of the department programmes Your main task as a PhD student will be to develop and complete a PhD
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seek a PhD candidate to work on representation learning methods on graphs for modeling static and temporal networks, with applications to ecological systems and beyond. The project will focus
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, cryo-electron microscopy (cryo-EM), cryo-EM-based polyclonal serology (cryo-EMPEM), molecular dynamics simulations, machine learning, and structural biology to define epitopes and engineer improved
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methodological development and application of bioinformatics, biostatistics, machine learning, and data management within clinical research. CLINDA is interdisciplinary and employs biostatisticians
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data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
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others. Essential: Strong data analysis and machine learning skills and experience with PyTorch (or equivalent frameworks). Hands-on experience with data representation and embeddings, ideally applied
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logical way and parametric simulation engines. Familiarity of machine learning / AI techniques and custom LLMs generation is beneficial. Knowledge of life cycle assessment (LCA) and LCA methods, carbon
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implement a hyperspectral imaging system tailored to bulk forensic trace analysis and develop chemometric and machine-learning models for material identification and classification. You will evaluate
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electrical engineering, control engineering, applied mathematics, computer science, or a related field A strong background in probability and statistics, machine learning, or control theory Interest in cyber
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One Health surveillance. Another key focus will be teaching at BSc, MSc and PhD level in microbial genomics, bioinformatics and machine learning, complemented by national and international training