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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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standardize large-scale multi-omics datasets and build databases Perform integrative and exploratory analyses of multi-omics datasets and apply machine learning methods to uncover underlying biological
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-of-the-art in SLAM, situational awareness, computer vision, machine learning, robotics, and related fields Developing and implementing innovative solutions, validated through real datasets and experiments
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both
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information see https://www.kuleuven.be/personeel/jobsite/jobs/60473129 Job description Design and implement chemometric and machine learning models (e.g., PCA, PLS-DA, clustering, CNNs) to classify
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Pneumatic Tires, Structure-Process-Properties Relationships. How will you contribute? Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling? You