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Field
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may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums, Collections and AI 3D Scanning and Digital Heritage Extended Reality (VR, AR and Mixed Reality
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matching, optimal transport or cell-cycle modelling. Key Responsibilities These include but are not limited to: Leading an independent research project in scientific machine learning and mechanistic
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focused on using spatial profiling and machine learning of human specimens in combination with functional experiments in animal models to understand cancer initiation, progression, and metastasis. We
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quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative omics analyses would be highly regarded if the candidate was not initially trained in
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Requirements: Candidates should hold a PhD in civil engineering, computer science, electrical/computer engineering, robotics, or a related field. Experience in computer vision, deep learning, 3D reconstruction
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: Guide junior researchers and graduate students. Required Knowledge, Skills and Abilities AI/ML Expertise: Strong knowledge of advanced machine learning, deep learning, and AI techniques. Programming
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Qualifications PhD. required. Additional Qualifications Experience/interest in programming language, verification, artificial intelligence or machine learning. Individuals with a demonstrated track record in
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a new computational paradigm that combines the versatility of the digital computer with the efficiency of close-to-physics computing. The group targets the full computational stack, from materials
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strong background in relevant areas. Key Responsibilities: To independently undertake research in computer vision and machine learning. To produce research reports and/or publications as required by
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for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi We invite