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to the Institute’s needs. Requirements for candidates: Proven experience in advanced hydrological modeling (e.g., conceptual or physically based spatially distributed models), particularly with SWAT+, SWAT-MODFLOW and
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they continue to investigate. Approaches include genetics, spatial genomics, single-molecule biophysics, super-resolution imaging, computational modeling, and structural studies including X-ray
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will lead research at the intersection of crop-switching, environmental resilience, and agrifood sustainability. The candidate will develop and apply spatial analysis, remote sensing, and modeling
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the
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Pytorch and/or JAX deep learning models. Experience in single-cell or spatial omics data analysis. What we offer Embedding within a computational team, with extensive experience in computational biology and
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analyzing large datasets of single-cell and spatial transcriptomics data to describe brain tumor progression from initiation to recurrence, functionally validating new therapeutic targets based
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Pytorch and/or JAX deep learning models. Experience in single-cell or spatial omics data analysis. What we offer Embedding within a computational team, with extensive experience in computational biology and
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particular emphasis on its contribution to biodiversity conservation and carbon sequestration under ecological restoration scenarios. The project will integrate spatial analysis, connectivity modelling, and
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sequencing instruments into microscopes! Your mission You will join our team, a leading group in the field of DNA nanotechnology specialized in creating molecular tools for life-science research (http
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shift a critical part of the spatial and photophysical information into the temporal domain, in order to drastically reduce the number of photons and the acquisition time required for image reconstruction