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encourage all qualified candidates, regardless of background, to apply! Job description The candidate will work on large-scale data analysis of cancer transcriptomics data (bulk, single cell and spatial), and
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; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE has numerous
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analyze the composition of the microbiota by regular sampling over the first year after transplantation. This analysis will be performed in collaboration with prof. Robert Jenq (City of Hope National
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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medicine in psychiatry, building on advanced statistical methods for analysis of large-scale human genetic and neuroimaging data, to better understand how biological, psychological, and environmental factors
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purification techniques. Proficiency in GC-MS and GC-IRMS analysis. Solid understanding of leaf wax isotope systematics (δD and δ¹³C) and their environmental interpretations. Proven track record of research
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collaboration with the theorists involved. The postdoc will construct and use 3D printed model porous media. The analysis will entail extensive use of image analysis techniques such as Particle Image
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specializing in epigenetic and epitranscriptomic modifications, DNA repair and genome (in)stability, and single-cell/nucleotide analysis, with a focus on cancer and embryology research. Research Project Overview
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Documented experience in image analysis Proven track record of publishing scientific articles Strong command of both written and spoken English Candidates who do not speak Norwegian must acquire proficiency in
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in cellular physiology, protein biochemistry and behavioral analysis as well as a basic background in R or transcriptome analysis. Duties of the position To conduct research in the lab responsibly