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PIs from Canada, Singapore, Netherlands, Switzerland, Norway and Germany. (https://www.schmidtsciences.org/view/#modal-mountainwater-water-from-the-mountains-global-reanalysis-and-future-tipping-points
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position in Chirag Patel’s group at Harvard Medical School in Boston, Massachusetts. The exposome is a promising and emerging modality to explain disease variation (papers in press in Nature Medicine
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training and inference. Model Interpretability & Optimisation: • Implement and evaluate feature attribution methods (gradient-based saliency, attention analysis, SHAP) to quantify the contribution
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mechanistic insights linking textural dynamics to sensory perception. Integrate multi-modal datasets from different mastication simulators into a 3D Principal Component Analysis (PCA) space, enabling
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interactions Expertise with human in vitro co-culture models, including CD8+ T-cell responses, is desirable Expertise in omics and systems immunology Proficiency in R or Python programming for data analysis
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include: Flow cytometry assessment of immune cell responses to stimulation. Design and optimisation of flow cytometry panels and analysis and interpretation of resulting data. Integration of flow cytometry
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modalities, enabling spectral properties, elemental distributions, and quantitative morphological characteristics to be studied within the same specimens. Using seaweed as a case study, you will investigate
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: F. Pelliciotti, ISTA, and W. Immerzeel, Utrecht, and further PIs from Canada, Singapore, Netherlands, Switzerland, Norway and Germany. (https://www.schmidtsciences.org/view/#modal-mountainwater-water
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://www.schmidtsciences.org/view/#modal-mountainwater-water-from-the-mountains-global-reanalysis-and-future-tipping-points ) various ESA projects including Glaciers_CCI, Permafrost_CCI, Karakoram Anomaly, and the forthcoming
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on understanding the earliest stages of high-grade serous ovarian cancer (HGSOC) by integrating spatial multi-omic profiling with computational analysis to define how precancerous lesions evolve into invasive