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) on the effects of a changing climate in Arctic ecosystems. We offer a working environment with world-leading excellence on a broad range of disciplines, strongly connected to a global network of arctic research
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power network. The project will be carried out in close collaboration with leading industrial partners, including Hitachi Energy Research, and will address the pressing challenge of maintaining power
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experimentation to address one of the most critical challenges in modern energy systems, maintaining stability in an increasingly converter-dominated power network. The project will be carried out in close
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data reflect real‑world disease phenotypes. Advanced analytics: apply AI and machine‑learning techniques (e.g., graph neural networks, multimodal transformers) to uncover novel biomarkers and generate
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present results from your research and develop your network. In addition to your own research and experiments, you will also supervise and help train new students when they join the group. This will help
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collaboration with Olof Lagerlöf’s group. The postdoc will image (and influence with opto-chemogenetics) the network representation of stimuli of different economic/hedonic value and its plasticity with two
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combination with machine learning and/or data mining techniques • Explainable AI/ML using visualization • AI/ML-empowered visual analytics of multivariate networks (network embeddings, …) • Large Language Model
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combination with machine learning and/or data mining techniques • Explainable AI/ML using visualization • AI/ML-empowered visual analytics of multivariate networks (network embeddings, …) • Large Language Model
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-smokers. The successful candidate will have a leading role in the analysis of bulk- and single cell RNA-seq data, miRNA seq data from scarce particle samples, multi-omics integration and network medicine
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prior to the application deadline Research experience with deep learning architectures (e.g. Transformers, diffusion models, graph neural networks) applied to multimodal data. Proven expertise in time