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to sign the contract. More information is available on: https://www.dges.gov.pt/pt/pagina/reconhecimento . Workplan and the objectives to achieve: This work aims to develop a numerical approach based on
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and structures in biomedical image data. Additionally, the candidate will analyze spatial transcriptomic data to evaluate spatial patterns of genes in tissues. They will primarily work on projects
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Neuroscience, or a related field—or an equivalent combination of education and relevant experience. 3+ years of experience fine-tuning spatial transformer networks, contrastive learning, model distillation, RLHF
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. The PhD (M/F), to be recruited in the context of the ERC StG MULTI-viewCELL, will be working on the development of a new method combining machine learning and biophysical modelling to model embryo
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. This data will be used as input in climate models to ultimately propose a spatial characterization of bike paths. The methodology will be tested in several areas in France and Switzerland and discussed with
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interested in mammalian stem cell biology, reproduction, and development. They use cutting-edge single-cell, spatial, in vitro, and surgical approaches in multiple model species and in human organoids
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-scale atmospheric conditions. Most current regional and global atmospheric chemistry models struggle to represent plume processes. Their spatial resolutions are typically too coarse to resolve plume-scale
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propagation in spatially periodic, non-dissipative media, such as electromagnetic, elastic, or acoustic waves in metamaterials. This decomposition can also be applied to analyse the growth or decay of flow
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collaborative research environment to analyze unique spatial data in an innovative manner. You will develop and perform cutting-edge bioinformatic analysis integrating different multiplexed spatial data, and gain
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studies according to the field. Possible research directions include: Development and implementation of digital twins, real-time sensor systems, and AI-based models to improve the safety of future