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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material
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classes on quantitative finance and machine learning); knowledge of English at a level sufficient to conduct classes for first- and second-cycle students; very good knowledge of R and Python programming
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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Knowledge, Skills and Abilities: Highly motivated, quick learner and someone who would be able to work independently and follow instructions accurately. Must be able to work with others and learn new
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computer vision, where the PhD project was fully or substantially method-focused on computer vision and/or AI-based image or video analysis have very strong knowledge of machine learning, with practical
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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. Knowledge of machine learning/deep learning (e.g., classification, feature learning, neural networks). Experience in scientific programming (e.g., Python and/or MATLAB) and code management tools. Good
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and good knowledge of opensource development practices; • solid understanding in at least one of the following areas: numerical methods, computational modeling, machine learning or continuum mechanics
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: Required Additional Knowledge, Skills and Abilities: Skilled at laboratory work. Knowledge of biobanking procedures and operations. Knowledge of lab record keeping and organization. Able to learn and