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combines spatial point process models for the emergence and arrangement of objects (including birth-death dynamics, merging, and non-overlap constraints) with methods from shape analysis, in particular
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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applications for a junior independent faculty position in computational biology, focusing on areas like spatial transcriptomics, 3D tissue architecture, cell-cell communication, with emphasis on characterizing
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. The project involves large-scale analysis of high-dimensional datasets, including: single-cell and single-nucleus RNA sequencing spatial transcriptomics (e.g., 10x Genomics, Xenium) germline and somatic
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, atmospheric forcing, and human activities. Measuring and modelling this complex ecosystem is particularly challenging because of its high spatial and temporal variability, which requires dedicated and adaptive
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. The relevant synaptic turnover and plasticity processes span spatial scales from the molecular up to the network level, and temporal scales from seconds to hours and beyond. The aim of this PhD project is to
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combining wave modelling, field observations, remote sensing, advanced mathematical methods and AI, the research will quantify spatial patterns of wave set-up, its statistical distributions and co-occurrence
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systems, GHG emissions, waste and/or sludge management from SMEs/SMEs.• Experience with Python for data analysis, model automation and spatial/environmental processing.• Previous experience in research
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 4 days ago
of Computational Biology (ICB) , as part of the Computational Health Center at Helmholtz Munich, is globally recognised for innovations in data analysis and modelling of biological systems and diseases, anchored
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. Parametric algorithms and ML models trained on simulation-derived and measured datasets will approximate key microclimate variables and associated human–bioclimatic responses across a wide range of spatial and