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nanofabrication techniques (e.g., electron-beam lithography, dry/wet etching, metal deposition). • Background in low-temperature transport measurements or optical characterization of materials and devices
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develops an adaptive AI-guided XR platform for capturing and transferring expert manufacturing knowledge. Your focus will be on developing AI methods for analyzing and modeling human workflows based on data
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computing and application of AI-based methods Basic knowledge in photovoltaics and solid-state materials for energy application Ability to work individually and in the team, to effectively collaborate and to
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the Leibniz Junior Research Group “ Probabilistic Methods for Dynamic Communication Networks“ (Head: Prof. Dr. B. Jahnel) starting as soon as possible. The position is within the Math+ project "Information Flow
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candidates must possess an Honours degree with a First Class [1A] designation, or a master’s degree with a substantial research / thesis component. Evidence of journal publications would be highly desirable
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, data science using EHRs, machine learning, data mining, and natural language processing are preferred. Job Description: Develop large language models and other methods and tools to effectively use
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consideration the elements presented for the last five years of activity falling within the scope of the specific areas of the tender (see Point 1) and will be performed according to the following criteria: 11.1
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studies and interactive AI systems. This position will be funded based on an initial 2-year contract + 2 years extension. The key idea is to apply theories, models, and methods from psychology to improve
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self-motivated and possess excellent English skills, also, be open to discuss new ideas in an interdisciplinary team and be flexible to apply various experimental methods. TUD strives to employ more
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, particularly in the context of large-scale critical infrastructures for communication and computing. They will develop and validate AI methods that are safe by respecting system boundaries and are prompt in