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advanced phenotyping, imaging technologies, AI-based analyses, and digital twins. The PhD candidate will work on spring wheat genotypes adapted to Norwegian and northern European growing conditions
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experience in marine and polar environments. Experience with image analysis and molecular techniques, as well as taxonomic knowledge of Arctic zooplankton. A record of peer-reviewed publications will be taken
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spatial structures, physical laws, high-dimensional imaging, and clinical covariates. Apply these methods to spatial transcriptomics and fluorescence imaging data to gain a more precise understanding of
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Science. More about the position Modern diffusion models underpin state-of-the-art generative AI for images and continuous data, yet principled diffusion-based methods for discrete sequences (e.g., DNA, RNA
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studies; (3) identify design principles for a potential prototype, informed by the parametric findings of the project. The focus system (roadside barrier or façade integration) will be selected based
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. The PhD candidates will focus on developing methods that utilize remote sensing and AI supporting precision forestry. Remotely sensed data, such as images, lidar, and photogrammetric point clouds acquired
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novel, modular statistical solvers to integrate domain-specific knowledge directly into latent variable models. Account for spatial structures, physical laws, high-dimensional imaging, and clinical
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build a closed-loop, verifiable self-healing prototype for cloud-native 5G/6G operations, evaluate it under realistic degradations (failures/underperformance), and produce results that matter for both
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combination of in vivo voltage imaging, behavior, opto- and chemogenetic manipulation, and ex vivo patch-clamp electrophysiology. The candidate will train mice in various decision-making tasks and record
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spatial memories and how these processes are influenced by addictive substances. You will use state-of-the-art in vivo two-photon imaging and electrophysiology to record neuronal activity in rodents