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, the recruited researcher will contribute to the development of a novel time-resolved fluorescence lifetime measurement approach, in close connection with methods based on single-molecule brightness analysis
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will be working in an experimental lab, performing data collection, analysis, and modeling of behavioral and electrophysiological data. Applications are invited to apply for a statistical data analysis
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particular emphasis on its contribution to biodiversity conservation and carbon sequestration under ecological restoration scenarios. The project will integrate spatial analysis, connectivity modelling, and
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the
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, and spatial), RNAseq (bulk, single-cell, and spatial), and other multi-omic approaches in collaboration with the appropriate institutional core facilities. In addition, cultures mammalian cell lines
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. quantitative and/or qualitative counterfactual-based approaches, Difference in Difference models, Qualitative Comparative Analysis, Bayesian hierarchical modelling); o Experience working with and synthesizing
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& AI hardware or brain-inspired AI algorithm development, spatial analysis of multi-omics data. We are particularly interested in applicants with a demonstrated track record of translating discoveries
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Pytorch and/or JAX deep learning models. Experience in single-cell or spatial omics data analysis. What we offer Embedding within a computational team, with extensive experience in computational biology and
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approaches from the Digital Humanities. The successful candidate is expected to apply and further develop methods such as spatial analysis, GIS-based research, and network analysis in order to explore
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diffraction and pair distribution function analysis, infrared spectroscopy, and µ-Raman spectroscopy. Chemical mapping and phase speciation will be evaluated by fluorescence and X-ray absorption spectroscopy