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of technological disruption driven by Artificial Intelligence, we propose to analyze the data and quantify these similarities by exploring various applications of machine learning methods. With the advancement of AI
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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, as well as doctoral programmes and several Master’s programmes. Open to students in initial education as well as to professionals through lifelong learning, these programmes are delivered by our three
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possess expertise in a broad range of topics in condensed matter, including magnetism, superconductivity, topological matter, and low-dimensional quantum matter. The candidate will learn and master a
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, and computer science). We do not expect any single person to be an expert across all these fields, but you must be open to learning about and embracing challenges in fields you may not yet have been
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expect any single person to be an expert across all these fields, but you must be open to learning about and embracing challenges in fields you may not yet have been working with. We thus warmly encourage
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are seeking a candidate holding a Master 2 degree in computational biophysics, structural bioinformatics, or a related field. Knowledge of statistical mechanics and/or machine learning would be an asset
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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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and validated within the PhLAM team. Consequently, only limited effort will be required to acquire the experimental skills needed for this part of the project, allowing the PhD candidate to focus
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modern machine-learning techniques, will be exploited to improve the discrimination between the different polarization states. The analysis will use the complete Run 2 and Run 3 datasets collected by