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Field
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far; strong gravitational lensing). Under the supervision of Prof. Martin Bureau, the postholder will primarily work with the international WISDOM team on high spatial resolution (~10 pc) ALMA studies
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. Integrates mass spectrometry imaging data with other multimodal imaging techniques (e.g., spatial transcriptomics) for comprehensive spatial analysis. Establishes new research protocols and procedures
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development. Key Responsibilities: - Develop and implement spatial analyses of environmental exposures - Create and validate neighborhood typologies - Analyze associations between environmental factors and
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-processing pipeline for high-field MRI medical data (normalization, denoising, spatial registration) to optimize the quality and consistency of data used in analyses, and to facilitate the search
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storage potential and future carbon removal and carbon farming policy scenarios. It integrates process-based knowledge with machine learning and especially aims to spatially quantify uncertainties. You will
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computational power and the increasing availability of large volumes of remote sensing data with finer spatial and temporal resolutions have significantly transformed the way we approach climate and weather
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image reconstruction methods, signal processing techniques, and data analysis pipelines for novel X-ray imaging modalities, including ghost imaging, quantum-enhanced imaging, and other correlation-based
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learning and deep learning methods to analyze multi-omics data (genetic, epigenetic, transcriptomic, imaging, single-cell genomics and spatial omics data) with the goal of understanding the underlying
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the nature of the ambient noise in the reactor (spectrum, spatial and temporal distributions, levels), the approach consists in developing passive methods by determining their conditions of use but not
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coordinated analysis of long-term ecological data to understand the drivers and consequences of global change on biodiversity. To unravel how populations, interactions between species in natural communities and