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Field
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recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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) Development of an Augmented Smart Classroom for Personalized Learning (SmartClass) serving as a test-bed for the collection and analysis of students and professors data, leveraging on data analytics and machine
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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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element methods. Additionally, expertise in computational fluid dynamics, solid mechanics and fluid-solid interaction is essential. Knowledge or experience in multi-scale modelling and machine learning
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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training and guidance to junior undergraduate and graduate students. Education: A PhD in Neuroscience, Computational Neuroscience, Machine Learning in image analysis, or a related field, with significant
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within the Clusters of Excellence ‘Machine Learning for Science’ and ‘Image-Guided and Functionally Instructed Tumor Therapies (iFIT)’. Requirements PhD in Bioinformatics, Computational Biology, or a
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic