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methods for eliciting and aggregating safety specifications and risk thresh-olds for AI systems, with a particular focus on mechanisms that provide axiomatic guarantees, are algorithmically tractable, and
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methods for eliciting and aggregating safety specifications and risk thresh-olds for AI systems, with a particular focus on mechanisms that provide axiomatic guarantees, are algorithmically tractable, and
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academic backgrounds and industrial experience in a broad variety of disciplines such as propulsion, avionics systems, system engineering and integration, mechanical systems, assembly, integration and test
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secure food system. You also possess: a PhD in Biology, Biotechnology, Bioengineering, Biochemical Engineering, or related fields, with demonstrated experience in animal cell culture; minimum of 2 years
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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, including grades. Your PhD thesis. Starting date of the position: flexible, currently aimed to start around February 2026. For more information regarding this position, you are welcome to contact Dr. Silke
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. Qualifications We are looking for a candidate with A PhD in Computer Science, Operations Research, Applied Mathematics, Mathematics, Engineering, or a related discipline. Strong programming experience, for example
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Technology and Production Automation, m.cao@rug.nl We are looking for candidates who meet the following requirements A PhD degree in Control Engineering, Applied Mathematics, environmental Psychology or a
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for this position, the following is required: PhD in systems engineering, computer science or informatics, and the subject of the thesis should be relevant to the task description provided above (e.g. digital twin