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) as Art Historical Tool Network Analysis and Cultural Data Human–AI Collaboration in Art Historical Interpretation Digital Provenance Research and Collection Histories Computer Vision for Art Historical
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to art historical research through artificial intelligence, immersive technologies, computer vision, computational analysis, digital heritage, or related methods. Salary and benefits £44,247 to £50,379
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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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and journals. Mentor graduate students and support research activities within SNAIC. Required Qualifications Applicants should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence
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should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep
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and Experience: Completed a PhD in a relevant field (e.g., synthetic biology, computational biology and AI, microbial, plant and human cell biology, genomics, robotics and automation, and nucleic acids
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Desirable Knowledge, Skills and Experience: Completed a PhD in a relevant field (e.g., synthetic biology, computational biology and AI, microbial, plant and human cell biology, genomics, robotics and
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Knowledge, Skills and Experience: Completed a PhD in a relevant field (e.g., synthetic biology, computational biology and AI, microbial, plant and human cell biology, genomics, robotics and automation, and
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biology, computational biology and AI, microbial, plant and human cell biology, genomics, robotics and automation, and nucleic acids chemistry.). Track record of delivering ambitious research projects to a
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, computational biology and AI, microbial, plant and human cell biology, genomics, robotics and automation, and nucleic acids chemistry.). Track record of delivering ambitious research projects to a high standard