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to tumour tissue images have improved characterisation of cancer tumours in clinical routine. However, traditional machine learning models require annotated data and are limited in scope, while foundation
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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation
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experiments, and machine learning (ML) to understand and predict multiscale transport phenomena in fuel cell systems. In particular, the postdoc will bridge pore-scale simulations and macroscale performance
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applying deep learning and/or machine learning models to medical imaging data. Other information This is a permanent position, 100 % of full time. Starting date in October 2025 or according to agreement. How
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dynamics and heat transfer research. Programming skills in Python and MATLAB, particularly in machine learning, data analysis, and image processing. Experience working in Linux environments. Ability
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biology, protein engineering, biochemistry. Optical engineering, fluorescence microscopy, image analysis: Development of microscopes and data analysis pipelines used to acquire and quantify high-throughput
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description and duties The postdoc fellow will conduct research at the borderline between the fields of information visualization / visual analytics as well as machine learning in close collaboration with
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innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. More specifically, at NRM this research will be
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Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg | Sweden | about 1 month ago
mechanisms in normal neural development (demonstrated by us and colleagues) and may harbor cues for novel treatment strategies. Omics data can be used in black box machine learning algorithms to classify or
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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