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models to quantify phenological responses. Collaborate with internal and external partners, including leading researchers in phenology and population biology. Mentor master's students participating in
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testing of high-fidelity models of endocrine-responsive breast cancer and analyses of multi-omic datasets spanning both models and clinical samples ● The successful candidate will be required to work in
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health and translational medicine using a "bench-to-bedside" approach. By harmonising and analysing diverse biomedical data, while focusing on the secure data processing and predictive modelling, we aim
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! PROJECT: The project entitled ‘Dynamic cues guiding postnatal germline development in marmoset‘ focuses on single-cell transcriptome analysis of germ cell development in a non-human primate model. In
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disorders, and childhood cancers. We employ high-throughput metabolomics, epigenomics, single-cell multiomics, (epi)genome editing, and preclinical models to define disease-associated metabolic and epigenetic
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in a group with a strong publication track record Work in a supportive, international environment just outside Copenhagen If you want to apply your skills in animal models and translational
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Pneumatic Tires, Structure-Process-Properties Relationships. How will you contribute? Do you have proven skills in data analysis, machine learning, as well as in mathematical and computational modelling? You
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neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be applied in close
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-scale screens to study fundamental principles in molecular and complex trait genetics using microbes as model systems. Our core technology MAGESTIC (https://doi.org/10.1038/nbt.4137 ), a CRISPR/Cas9-based
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 1 month ago
, mathematics, and data science. Collaborative projects merge traditional geographic research with advanced computational methods such as graph neural networks (GNNs) and large language models (LLMs) to explore