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Instituto de Geografia e Ordenamento do Território da Universidade de Lisboa | Portugal | 4 days ago
submission and publication processes, with the adoption of a fully digital model beginning in 2026. The editorial choices made aim to enhance the journal's appeal to authors, reviewers, and readers, as
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training network and aims to apply mathematical modelling methods to study adrenal gland steroid biosynthesis dynamics and their spatial relationship with adrenal tumours found in Primary aldosteronism (PA
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analytical models. A deep understanding of environmental systems, spatial data processing, and the integration of multi-source information will be essential to ensure high-quality, science-based outcomes
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spatial transcriptomics derived from rodent models or human samples. o Oversee data processing, quality control, and computational analysis to ensure high accuracy, reproducibility, and adherence to best
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biology to pioneer research in immunology using single-cell and spatial transcriptomics data. The focus will be on development of novel computational methods for gaining fundamental insights into healthy
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee Our department is looking for a Research
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-of-the-art in vivo perturbational technologies and advanced computational modelling to solve fascinating biomedical puzzles. This interdisciplinary atmosphere has been a main catalyst for many past successes
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project Socio-Spatial Situatedness of Roman Professions and its Impact on Religion in the Roman Empire: A Formal Modeling Approach (SIPROME). The SIPROME project investigates how professions and the socio
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 13 hours ago
nebulae, protoplanetary disks—but also from exoplanets and galaxies. The data and tools will enable interpreting observational data from NASA missions, notably, JWST, which is now spectrally and spatially
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Learning methods for modeling spatial and spatio-temporal data, including point pattern data, possibly observed over complex domains. The candidate will specifically consider physics-informed statistical