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Qualifications: The successful candidate must hold a PhD in Computer Science, Artificial Intelligence, or a closely related field, and demonstrate a strong background in computer vision and machine learning
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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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indefinite employment contract within the framework of a Line of Research R&D line: MACHINE LEARNING APPLICATIONS https://www.upv.es/entidades/SRH/conypi/A1274520.pdf Where to apply Website https://sede.upv.es
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for more than 12 months in the 36 months immediately prior to your recruitment. Skills: Strong interest in AI/Machine Learning, Bayesian modeling and decision-making. Benefits Competitive Salary: Living
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this goal, doped-diamond systems will be considered. The thermal stability of selected compounds under operating conditions will be assessed by means of molecular dynamics simulations with Machine Learning
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certificate recommendation letters, if available (max 3) writing samples (max 3), e.g. PhD thesis and published papers Your Profile strong background in machine learning/artificial intelligence experience in
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Description Your Responsibilities We are looking for a highly motivated PhD student in the areas of Probabilistic Machine Learning and Neuro-Symbolic AI to contribute to the Cluster of Excellence “Bilateral AI
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methods, complemented by simulations of beta-decay chains relevant to post-fission energy release. Neural networks and other machine learning techniques will accelerate the discovery of radiation-resistant
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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | about 2 hours ago
machine learning Experience in computational methodologies for at least one or more omic technologies At least one peer reviewed publication LanguagesENGLISHLevelGood Additional Information Additional
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efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices. Still, many challenges are ahead