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and implement machine learning systems, models, and schemes Research and apply state-of-the-art machine learning prototypes to sponsor specific domains Search and select appropriate data sets before
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: Human postmortem brain and spinal cord tissues Spatial transcriptomics Spatial proteomics (CODEX) Single-cell RNA sequencing Human iPSC-derived microglia and motor neuron models CRISPR/Cas9 genome
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offers the opportunity to work in a highly collaborative and interdisciplinary environment with access to cutting-edge technologies, including patient-derived glioblastoma models, spatial transcriptomics
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, small-molecule and biologic discovery and/or delivery, development of organoid and animal models, and engineering of advanced targeted technologies for therapeutic use including but not limited to (epi
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in the spatial organisation of chromatin and genomic instability in healthy and cancer cells. The project will use TET1 knockout, catalytically inactive, and overexpression models. Key responsibilities
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To make reliable predictions and analyses of air pollution, data assimilation methods, which integrate observational data into model simulation to update the model state and the underlying
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data with long-term demographic datasets (e.g., ForestGEO data) Spatial analysis and ecological modeling (neighborhood models, density dependence, spatial statistic Experience with whole-genome
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developing innovative probabilistic methods to tackle fundamental problems at the intersection of spectral geometry, mathematical physics, and number theory. By exploring the geometry and spatial features
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to an international, interdisciplinary doctoral network. Excellent written and spoken English and strong scientific writing skills. Experience with EU biodiversity or sustainability policy, spatial modelling, social
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cytometry, microscopy, bulk and single cell RNA-sequencing, spatial transcriptomics) to study the host response against Mtb infection using mouse models in a BSL3 facility. Perform basic bioinformatics