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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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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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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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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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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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-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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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
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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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of agricultural weeds to herbicdes from an eco-evolutionary perspective. This project will develop models for the evolution of herbicide resistance that combines field data and computer models. The aim is to