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. Eran Elhaik to design machine-learning models that unlock the potential of genomics for forensic investigations and historical reconstructions. Work duties We aim to develop machine learning methods
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improve models and codes for assessment of the liquid source term during severe accident (SA) of LWR systems. In particular, research will include assessment of the characteristics of the containment water
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epidemiology to understand RNA metabolism. Perform stochastic simulations to analyze model behaviors. Fit the model parameters to empirical RNA expression and RNA-protein binding data. Predict outcomes
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reacting flows. A novel aspect of the project is the use of highly perturbed laminar flame simulations to inform CFD modelling of turbulent combustion in lean hydrogen-air mixtures. Experimental work will be
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educational programs, we are now seeking a postdoctoral researcher to work on privacy for data-driven models and high-dimensional data. The position is full-time for two years, starting on 1st September, or as
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design. One of our group's goals is to create efficient surrogate models that reduce the computational cost of MD simulations by several orders of magnitude. Notable examples of our work in this area
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networks are controlled, to develop predictive models of methane cycling in northern rivers. This postdoc position will focus on assessing how stream methane emissions are linked to permafrost thaw, using
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evolution, and further to modelling. Investigating transcriptional profile of lncRNA. It can be expression quantification, alternative splicing, start sites properties. Performing AI modeling and developing a
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spinal cord as a model system. You will engage a systematic strategy to identify these mechanisms by generating innovative mouse genetic strains, identifying embryonic defects and the underlying molecular