- develop novel machine learning models that integrate mechanistic prior information and knowledge in a principled manner
- apply these methods to large single-cell variation datasets with millions of cells, and to integrate spatial technologies with single-cell RNA-seq and epigenome methods
- contribute to the Human Cell Atlas, as a node in the analysis working group
- collaborate with partners in the MechML projects, the Human Cell Atlas, collaborators at EMBL, DKFZ and elsewhere
- build on previous developments and expertise in the group, including factor model, linear mixed models and deep learning methods
Recent relevant publications:
- Argelaguet, R., et al. (2018). Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets. Molecular Systems biology, 14, e8124.
- Svensson, V., et al. (2018) SpatialDE: Identification of spatially variable genes. Nature Methods, 343–346.
- Buettner, F., et al. (2017) f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq.” Genome biology 18.1 (217): 212.
- Angermueller, Christof, et al. (2017) DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome biology 18.1 (2017): 67.
- Buettner, F., et al. (2015). Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature biotechnology, 33(2), 155.
- a doctoral degree or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science
- previous experience in developing and applying computational methods applied to large datasets
- prior experience in developing statistical methodology in a genomics context, including gene expression analysis, factor models, GWAS and analysis of NGS data.
- previous usage of methods in any of the following fields is advantageous: statistics, machine learning, genetics, optimization and mathematical modeling
- proficiency with a high-level programming language (e.g., C++, Java) and/or appropriate scripting languages, and statistical data analysis tools such as R, MATLAB or Python
- ability to work independently and creatively
- excellent communications skills and be able to articulate clearly the scientific and technical needs, set clear goals and work within an interdisciplinary setting, communicating with other partners within the MechML project and within the Human Cell Atlas project
You might also have
- expertise in analysis and integration of multiomics data, statistical genetics, statistical interpretation and analysis of next-generation sequencing datasets and an ability to communicate results in scientific conferences and papers
- a background in molecular biology, or previous experience tackling biological questions
Why join us
What else do I need to know
Our mission is to offer vital services in training scientists, students and visitors at all levels; to develop new instruments and methods in the life sciences and actively engage in technology transfer activities, and to integrate European life science research.
Please note that appointments on fixed term contracts can be renewed, depending on circumstances at the time of the review.
|Staff Category:||Postdoctoral Fellow|
|Contract Duration:||3 years|
|Closing Date:||4 November 2018|