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- University of Oslo
- NTNU - Norwegian University of Science and Technology
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- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- NTNU Norwegian University of Science and Technology
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algorithms to jointly optimize both the robot’s morphology and autonomy, and apply quality-diversity methods to discover a wide range of high-performing designs. Work on genotype-to-phenotype mappings
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(AI), and therefore a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models and algorithms
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needs of the research center. More specifically, the postdoc will work on the following topics, in collaboration with the rest of the team: Develop and apply evolutionary algorithms to jointly optimize
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models and algorithms that integrate general and domain-specific knowledge with data
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topics include (a) AI, machine learning, and large language models for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant
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on their implications for education and teaching. We are seeking a postdoctoral project that analyzes how algorithm-driven and AI-powered platforms influence public discourse. The project may focus on processes
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mathematical and computational engine of Artificial Intelligence (AI), and therefore a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world. Integreat
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over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied to machine learning algorithms in order to get uncertainty estimates for parameters governing
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that integrate prediction and control algorithms, optimizing data transformations, offloading and distributed computing, and exploiting mechanisms such as network slicing and multi-access edge computing
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without a master’s degree have until June 30, 2026 to complete the final exam. Desired qualifications: Experience with data simulation, clustering algorithms, benchmarking, model selection and evaluation