PostDoc Position – Innovative Computational Methods: The Big Data in BioMedicine Group at the Chair of Experimental Bioinformatics (School of Life Sciences, Technical University of Munich (TUM)) is seeking a Postdoctoral Researcher to develop innovative algorithms for the joint analysis of single-cell & spatial transcriptomic data in the framework of the Novo Nordisk funded collaborative data science grant “MOPITAS” – Multi-omics profiling in time and space. The position is available for up to 4 years.
Summary Table:
| Job Title | PostDoc Position |
|---|---|
| Designation | Researcher |
| Research Area | Bioinformatics |
| Location | München |
| Eligibility/Qualification | PhD in Computer Science, Bioinformatics, Molecular Biology or similar |
| Job Description | Develop innovative algorithms for the joint analysis of single-cell & spatial transcriptomic data |
| How to Apply | Send a dossier containing a motivational statement, CV, list of publications, certified copy of PhD degree certificate, and at least one referee to Dr. Markus List via email. |
| Last Date for Apply | March 10th, 2023 |
Designation: Researcher
Research Area: The development of innovative algorithms for the joint analysis of single-cell & spatial transcriptomic data in the framework of the Novo Nordisk funded collaborative data science grant “MOPITAS” – Multi-omics profiling in time and space.
Location: München
Eligibility/Qualification:
- A PhD in Computer Science, Bioinformatics, Molecular Biology or similar.
- Strong analytical and problem-solving skills.
- Strong written and oral communication skills (in English).
- Experience in bioinformatics method development and analysis of single-cell sequencing and spatial transcriptomics data.
- Extensive knowledge of and practical skills in statistical analysis, data mining, data integration, machine learning, and programming in R or python are a plus.
Job Description:
PostDoc Position – Innovative Computational Methods for the Joint Analysis of Single-Cell & Spatial Omics Data (m/f/d)
The successful candidate will work on the development of innovative algorithms for the joint analysis of single-cell & spatial transcriptomic data, bridging the gap between Spatial Transcriptomics (ST) and single-cell RNA sequencing. The project aims to incorporate information on gene-regulatory activity via scATAC-seq and utilize novel and innovative algorithms based on a complex interplay of deep neural networks to decipher location-specific gene-regulatory mechanisms. The focus of TUM in this collaborative project will be to unravel how tissues form and change over time and space.
How to Apply: Applicants should send a dossier containing a motivational statement (max. one page), a curriculum vitae summarizing qualifications and experience, a list of publications, a certified copy of a PhD degree certificate, names, and the email addresses of at least one referee as a single PDF document to Dr. Markus List (via e-mail to markus.list [at]tum.de). Online interviews will be conducted with selected candidates, and remaining shortlisted candidates may be invited to the TUM campus in Freising, Germany, for face-to-face meetings with the group.
Last Date for Apply: 10 March 2023.
