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advanced statistical approaches. The doctoral project will be developed jointly with the successful candidate and tailored to their methodological background, previous research experience and scientific
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and damaging storms, and cascading risks such as bark-beetle outbreaks. Forest owners and regional authorities need spatially detailed, timely information on where climate stress is emerging, and which
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modelling, automated neuroanatomical phenotyping, machine learning and advanced statistical approaches. The doctoral project will be developed jointly with the successful candidate and tailored
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try again. UiO/Anders Lien 16th October 2026 Languages English English English PhD in spatial genome organization and gene regulation PhD fellowship Apply for this job See advertisement About the
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appointment. Candidate’s CV must state qualifications or if pending upon submission. Preferred Qualifications: Proven experience analyzing scRNA-seq, scATAC-seq, spatial-RNA-seq or other genomic data. Proven
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/Engineer 3 (RS/E 3) in the Zhu Laboratory leverages state-of-the-art microphysiological systems, imaging technologies, and single cell RNA sequencing and spatial transcriptomics to study host immune
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computational analysis of functional genomics datasets with an emphasis on single-cell omics. This person will develop new computational methods to analyze novel RNAseq, ATACseq, and Spatial Transcriptomics
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research teams. Responsibilities: Develop and apply AI-assisted weather and climate models with high spatial and temporal resolution. Integrate remote sensing, observational, and numerical model datasets
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communication skills and the ability to organise your own work are essential. Experience with demographic modelling, ecological databases, functional traits, spatial/environmental data or field ecology is desirable
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statistical models (Bayesian approaches, geographical analyses) adapted to environmental data. • Carry out statistical analyses and the spatial distribution of risk between residential environmental exposures