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are excited to push the boundaries of responsible AI. Learn more about the lab's work at: https://martinpawelczyk.github.io/ . Tasks and Responsibilities Develop machine learning methods and tools with a
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The Computer Vision Group is looking for an aspiring PhD to investigate multi-agentic AI, LLMs, and VLMs applied to agricultural sciences. Currently, established AI models often fail to generalize
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Engineering, Mechatronics, or Robotics, with a heavy emphasis on dynamic system theory, or a closely related discipline. Strong academic background in applied intelligent control techniques, machine learning
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Modelling, Applied Statistics, Linguistics, Data Analysis, Large Language Models, Machine Learning. Start date: 1st October 2026 Deadline: 30th April Duration: 36 months Funding: Funded Funding towards
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Science, Machine Learning, Finance, FinTech, Economics, or a related field. Candidates should demonstrate knowledge of Large Language Models, generative AI, and machine learning, with interest in financial
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statistical modelling of high-dimensional data, e.g. penalised model selection and machine learning. Demonstrable understanding of RNAseq and gene expression analysis. Experience/skills handling and securely
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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information theory, and quantum technologies. For additional information, please visit: https://dakic.univie.ac.at/ . Your future tasks: You will actively participate in research, teaching & administration
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, this approach needs to be reconsidered and adapted for the developing brain. To do so, we will use high-quality MRI data in a large cohort of infants (N~1000), including subgroups with clinical conditions. Our