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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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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
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., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant response behavior (such as rapid guessing, cheating, or careless responding), (c) estimation methods
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limits and human perceptual tolerances. The work will comprise designing networking and computing architectures that integrate prediction and control algorithms, optimizing data transformations, offloading
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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exam. Desired qualifications: Experience with data simulation, clustering algorithms, benchmarking, model selection and evaluation workflows is an advantage Language requirement: Good oral and written