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Field
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Nancy and the long-standing experience in sophisticated computer simulation studies from Leipzig, promising unique prospects in advanced education of PhD students via research into this important field
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in the course of the disease would result in better outcomes for the patient. We are looking for candidates to research computer vision and artificial intelligence techniques to create an objective
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after employment. Required selection criteria You must have a relevant Master's degree in Reliability, Availability, Maintainability, and Safety (RAMS) or Cybernetics or Computer/Information Science with
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programming and/or software engineering Electrical engineering and/or power systems. Application Procedure Informal enquiries are encouraged and should be addressed to Dr Jack Umenberger (jack.umenberger
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. The project will contribute to research within human–computer interaction and human factors on topics including: human–AI teaming in safety-critical work AI transparency and explain-ability in operational
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and information sciences, mathematics, and music. We develop computational models for musical structures to understand music as a fundamental human trait, and apply these musical structures in novel
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the life sciences and related disciplines (e.g. biomedical sciences, biology, biochemistry, physics, chemistry, psychology, computer sciences, engineering) Acceptance by an authorised academic supervisor
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to design and evaluate Artificial Intelligent (AI) systems with and for people, aligning AI technologies with human needs and values, situated at the intersection of AI and Human-Computer Interaction
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digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap