Sort by
Refine Your Search
-
focus in multidisciplinary research. The CMCB laboratory aims more specifically at developing cutting-edge data/image analysis as well as modelling strategies to answer fundamental biology issues with
-
to facilitate the imaging analysis. The position further involves regular and effective communication of results both within and outside of the immediate research environment, as well as collaboration with other
-
incorporate it into mathematical models of trait evolution across phylogenies. The work combines dimensionality reduction and geometric data analysis with the development of statistically rigorous comparative
-
%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model
-
at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
-
assays, complemented by mass-spectrometry-driven chemical profiling and machine-learning-supported multivariate analysis. Where relevant, CRISPR-Cas-based genetic perturbations in mammalian cell models
-
processes. Projects can include assembling, sharing, integrating, and advanced analysis of large amounts of data from diverse sources, including experiments, observations, and simulations, to gain a deeper
-
of the methods for the different case study workshop in collaboration with the other consortium members and tailor it to the needs of the various stakeholders. They will be responsible for the analysis of results
-
. Required competencies: Strong background in bioinformatics (e.g., R, Linux, Python). Experience working with large cohorts and high-dimensional data. Experience with microbiome analysis and/or GWAS
-
). Experience working with large cohorts and high-dimensional data. Experience with microbiome analysis and/or GWAS. Excellent English communication skills, both written and spoken. Meritorious (preferred