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to this third-cycle studies which corresponds to four years. Position description We are seeking a PhD student to join our research team specializing in the analysis and modeling of multiphase flows. The research
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is on analysing first-person descriptions of conscious experiences with the help of machine learning and large language models (LLMs) to identify, compare, and systematize different types of states of
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essential tool for training and testing of AI models and control systems for robots and autonomous vehicles. In a digital environment, large amounts of annotated training data can be created safely and easily
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essential tool for training and testing of AI models and control systems for robots and autonomous vehicles. In a digital environment, large amounts of annotated training data can be created safely and easily
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this, we focus on self-supervised denoising, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural
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directions include: Quantitative genetics and phylogenetics: incorporating developmental constraints into evolutionary models and exploring how they shape patterns of variation. Modeling development from data
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. Work duties The main task is to conduct research. Teaching may also be included in the duties. Work in this field includes the design, modelling, realization, and characterization of nanophotonic
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of TrustLLM a large EU project developing trustworthy and factual language models. Host of the largest basic research program in Sweden Wallenberg AI, Autonomous Systems and Software Program (WASP) as
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large language models (LLMs)—that is, the inability of a model to effectively process or understand visual information. This work involves integrating visual encoders with language models to create
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pipelines. Tools for robust data and model provenance in adversarial environments. Methods for protecting training data and end users, including secure data removal and machine unlearning. Machine unlearning