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data (e.g., synchrotron X-ray microtomography), Design and use of autoencoders (VAEs, GANs), diffusion models, and other ML methods for analyzing and discovering patterns in probability distributions in
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are looking for a new colleague. As an ecologist you will perform applied and policy relevant research focusing on monitoring, modelling, protecting, and restoring biodiversity in (agricultural) landscapes and
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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model
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, metabolomics, clinical samples, and animal models to accomplish these goals. The results will lead to the development of novel therapeutic strategies for the clinically relevant molecular subsets of lung cancer
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- and post-operative management and assessment of animals after transplant (micro)surgical procedures. Experience with mouse models of survival surgery is necessary. For appropriately qualified candidates
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Perform molecular cloning, protein expression, and biochemical assays Conduct growth chamber experiments with model plant species Analyze genomic and transcriptomic data using computational tools Present
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particles - in promoting inflammation and fibrosis, and work to identify specific biomarkers of exposure and associated risks in human and mouse models. Develop and evaluate therapeutic strategies to target
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(e.g. compound muscle action potential). Experience in generating and/or working with both animal models and human models of Motor Neuron Disease Ability to work collaboratively with colleagues Ability
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale