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Meritorious will be considered experience in: - Microscopy-based imaging - Immunohistochemistry - Work with clonal cell lines - Work with other model organisms such as D. melanogaster or D. rerio You are a
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to address critical issues related to the reliability and quality of large language models (LLMs) such as ChatGPT and Gemini. These models are increasingly used in essential sectors like healthcare, education
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(MMTC) – provide PhD students with opportunities for specialized affiliation. Located in central Jönköping along Lake Vättern, JIBS offers a high-quality work environment combined with a lively urban
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. The research school for stress response modelling in IceLab Starting in the spring of 2025, Umeå University’s interdisciplinary research hub, IceLab, will offer doctoral positions through the new Stress Response
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of quick clays. A novel combination of miniaturised thermal-hydro-mechanical experiments and particle level modelling will be pursued to unravel the unique mechanisms that make quick clays so hazardous and
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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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address outstanding questions on behavioural evolution in canids. Your work assignments Understanding how behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model
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: Verifiable training and trustworthy AI pipelines. Tools for robust data and model provenance in adversarial environments. Methods for protecting training data and end users, including secure data removal and
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behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model species, the PhD student selected for this project will investigate unanswered questions on how complex
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. 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