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substantial opportunities for scientific growth, international networking, and career development, including support for developing independent research ideas and applying for external funding. Read more on the
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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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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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) 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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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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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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, electrophysiology, chemo-optogenetics. Behavioral measurements will be performed in collaboration with Olof Lagerlöf’s group. The postdoc will image (and influence with opto-chemogenetics) the network representation
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of progenitor-to-tumor cell niche transitions, focusing on the esophageal epithelium. Duties The current project will be centered around characterizing changes in local cell-cell networks during early esophageal
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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