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multiome RNA-seq, ATAC-seq and massively parallel reporter assays (MPRAs) for unbiased genome-wide analysis for understanding the phenotypic plasticity in different cancer cell states. Work tasks The work
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to improve the modelling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and incomplete data
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philosophy to improve the modelling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and
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the modeling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and incomplete data sources, and
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to integrate experiments ranging from detailed mechanistic cell biology to in vivo imaging of GBM invasion. The aim of the project is to generate novel mechanistic insights into the cell biological processes
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until 2033. The candidate will pursue research on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference and
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the EU to develop more active and discretionary capabilities. We will study these processes of change from both inside the EU (with interview research on the EU’s evolving policies regarding tradable goods
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with the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The research group on statistical models
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motivation to carry out the project and teach at the department. The hiring process will include an interview. Applicants must have a PhD degree in human geography or equivalent. Only candidates who received
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Qualification requirements PhD or equivalent academic qualifications with a specialization in computational musicology, computer/data science, informatics, sound and music computing, or other related field