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, funded by FAR 2024 UNIMORE linea FOMO, and aims to develop authenticity models through environmentally friendly analytical techniques combined with data processing and machine learning algorithms
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. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning. Your tasks: Development and comparison of data driven models for the prediction of stresses in
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, and/or multiphysics modelling • Mathematics & AI: Numerical analysis, inverse problems, neural networks, scientific machine learning • Programming: Python (scientific computing, ML), preferably C
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multiscale modeling via machine learning force fields. This research will focus on applying a range of computational tools to realistic material systems, including interfaces and defects. This research will
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are seeking an experienced and highly skilled Data Scientist with a strong foundation in genomic biostatistics to join our team. This role involves leveraging advanced statistical methods and machine learning
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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reclamation pilot-scale and lab-scale systems. Conduct membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning-based
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. Excellent experience in membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning based modelling. Relevant research
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to be part of a unique and diverse community that works on high-impact research, educational, business, and societal problems. Position Summary The Machine Learning Researcher - Epidemiology will report
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-resolution (SR) technologies influence human and machine-based facial identification. The PhD will combine behavioural experiments, machine learning, and explainable-AI methods to answer questions: 1. Do SR