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Field
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-of-the-art machine learning methods, acoustic sensing can provide valuable insight into a wide range of processes occurring within the built environment. Potential applications include structural health
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requirements Essentials: Basic knowledge of power systems and machine learning/AI. Completed an MSc degree in a highly technical related discipline (computer science, electrical engineering, etc.) and you were
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generation of experts skilled in the combination of Operation Research and trustworthy Machine Learning. CoRDS goes beyond the state-of-the-art DDO methods by proposing decision support frameworks that combine
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Information Management, or related fields; Have basic knowledge of machine learning models in supervised and unsupervised learning tasks (i.e., k-nearest neighbours, Decision Trees, Neural Networks, Logistic
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master in Computer Science, Bioinformatics or related field Strong programming skills in R Background in machine learning and/or protein structures Fluent in English Specific Requirements Mobility rule
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results in scientific journals You contribute about 10% of your time to teaching and supervising in bachelor and master programs Where to apply Website https://www.academictransfer.com/en/jobs/362571/phd
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PhD candidate you will develop new ways to extract cosmic-ray physics from KM3NeT data. You will design and characterise reconstruction methods—both machine-learning-based and traditional
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of AI/ML into application and system software. Join a high-impact research team and help shape the next generation of dependable, efficient embedded systems. Learn more and get in touch: https
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engineering stimulated by recent developments in machine learning, the project investigates how runners’ shared social identity can influence their movement synchrony, physical load, and interaction with
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing