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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 16 days ago
motivated postdoctoral researcher (f/m/x) to develop advanced physical and computational models integrating transcriptional and biomechanical data during mammalian gastrulation. This position is part of
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(n = ~1 million adults; 188,402 deaths observed), modeling spatial processes, and considering family risk factors. Duties of the postdoctoral associate include preparing area-level data, data analysis
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(n = ~1 million adults; 188,402 deaths observed), modeling spatial processes, and considering family risk factors. Duties of the postdoctoral associate include preparing area-level data, data analysis
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LiDAR-based ecology. We're looking for candidates with strong technical skills and ecological interest—people who want to use LiDAR, AI, and spatial modeling to advance our understanding of vegetation
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. These data will be integrated into TemisFlow (Themis) thermal models to reconstruct the thermal and subsidence history of the basins. The modeling will quantify the distribution of heat flow during
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funding. Appointment Start Date: Fall 2025 Group or Departmental Website: https://hph.stanford.edu/careers/ (link is external) How to Submit Application Materials: Submit all application materials
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situ or even operando (laboratory and Synchrotron), and modeling tools (Monte Carlo, molecular and Brownian dynamics, machine learning), allowing, as often as possible, spatially and temporally resolved
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interdisciplinary research group dedicated to developing integrated approaches to geospatial systems analysis. Our team pushes the boundaries of how spatial data can be used to tackle today’s pressing environmental
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impacted their genomic diversity. The project focuses on Lepidoptera (butterflies and moths) as model system, that are currently threatened or vulnerable, which serve as important ecological indicator. By
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an outstanding and ambitious postdoctoral researcher in computational biology to pioneer understanding and modeling of tissue architecture using single-cell and spatial transcriptomics data. The focus will be