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Field
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-based products, is among the areas that more quickly is adopting AI. Machine learning (ML) algorithms are being developed and integrated in microscopes for its autonomous operation and in software
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and interpretable quality assessment algorithms. This research combines various machine learning topics, including uncertainty, explainability, and fairness in supervised and unsupervised deep learning
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for improved understanding of structural and kinetic processes in electrolytes; and machine learning concepts for improved analysis of experimental and simulated data. Material Synthesis Within this research
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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
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. Ideally you have Programming skills and knowledge on machine learning and statistical data evaluation, creation of scientific programme codes using common software packages (MATLAB, Python, R) Simulation
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data-driven machine learning Your Profile: Completed Masters degree in chemical engineering, computational engineering, computational mathematics, data sciences / analysis, system or process engineering
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different sources (observation, model output) You are interested in applying methods for automated detections of weather (e.g., cyclones, jets, frontal systems) which can include machine learning methods You
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development of artificial intelligence (AI) software for topology-informed biomedical image analysis and large foundation models. You will be responsible for Develop new machine learning algorithms
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oceanography), chemistry, ecology, mathematics, computer science, engineering, economics, or political science Since the programme focuses strongly on numerical simulations, candidates with computer literacy
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professionals in industry, government, and academia. Globally, geospatial technology is often mentioned as a high-growth industry sector. Career chances for graduates with GI-related skills can therefore be