Sort by
Refine Your Search
-
knowledge for precision medicine in brain disorders, building on advanced statistical methods and AI-tools for analysis of large-scale human genetic and neuroimaging data, to better understand how biological
-
for analysis of large-scale human genetic and neuroimaging data, to better understand how biological, psychological, and environmental factors contribute to severe mental and neuropsychiatric disorders
-
Deviations” (TOMABOLD), funded by the Research Council of Norway. The PhD position will focus on the large deviation analysis of probabilistic models, and associated problems in PDE, with emphasis
-
PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
background in one or more of the fields of rock physics, petrophysics, seismic attribute analysis, seismic inversion (both pre- and post-stack inversions), and machine learning with geoscience knowledge
-
deviation analysis of probabilistic models, and associated problems in PDE, with emphasis on identifying both well- and ill-posed examples and the interplay between probabilistic analysis and the analysis
-
experience with building Docker containers. Experience with high-throughput sequencing data analysis (e.g., CAGE, ATAC-seq, ChIP-seq, or Hi-C). Familiarity with epigenetics, gene regulation, or chromatin
-
experience with building Docker containers. Experience with high-throughput sequencing data analysis (e.g., CAGE, ATAC-seq, ChIP-seq, or Hi-C). Familiarity with epigenetics, gene regulation, or chromatin
-
programming and computational modelling as core elements. Deadline 6th January 2026 Employer University of Oslo Municipality Oslo Scope Fulltime (1 positions) Fulltime (%) Duration Project Place of service
-
to do joint end-to-end analysis of a wide range of cosmological datasets, including archival data such as AKARI, DIRBE, FIRAS and Planck, ongoing projects like COMAP, PASIPHAE, Simons Observatory, SPHEREx
-
of bottom-up liquid chromatography-mass spectrometry (LC-MS) analysis of established disease biomarkers. Novel MIP-assays developed in the project will be validated against conventional assays (including both