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of computation, and thus continuous aspects, into rule-based models of graph transformation in order to combine the individual strengths of both paradigms. Rule-based models are transparent and explainable
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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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navigation; Advanced III-V semiconductor nanowire technology that exploits light to obtain a large number of interconnects with extremely low power consumption; Optically efficient stable molecular dyes
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
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) exhibits a unique operational mode in that a p-n junction doping structure forms during the initial device operation. The PhD project will be performed at the Swedish SME LunaLEC AB (www.lunalec.com ) and
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qualifications You have graduated at Master’s level in computer science or completed courses with a minimum of 240 credits, at least 60 of which must be advanced courses in computer science, mathematics, AI
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children and young people, and on changes in technology and ways of working. You will conduct semi-structured interviews and focus-group discussions with psychologists and school counsellors within services
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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
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knowledge and model structure developed within phase 1 of the research program Mistra Food Futures (mistrafoodfutures.se) and include extensive collaboration with various researchers and external
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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