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insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
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, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree (PhD), it is important
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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree
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of organic chemistry, molecular structure, and/or drug-discovery principles. Demonstrated interest in applying machine learning or computational methods to chemical or biological problems. Motivation to work
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, Ultrasound and Vibration, Aircraft Structures, Damage Assessment, Structural Health Monitoring, Structural Health Prognosis, Bayesian Statistics, Machine Learning Informal enquiries prior to making
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inverse problems. The team aims at developing Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In
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project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets
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produced via traditional casting, extrusion and forging. Reports on rationalisation of processing-structure-property relationships (PSPR) additively manufactured (PBF-LB) Mg alloys are still scarce. In
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datasets from Kepler, TESS, and PLATO to reassess trends in exoplanet occurrence, structure, and evolution. Methods and Tools The project will involve: • Bayesian inference (hierarchical models, posterior
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to improve R&D efficiency, and the influence of investors and other external actors on entrepreneurial outcomes. Our research also examines decision-making under uncertainty, including the use of Bayesian