Postdoctoral Position in Evolutionary Genomics and Bioinformatics (f/d/m)
Medizinisches IK-Zentrum
Herrn Martin Schneider
Krankenhausstraße 12
91054 Erlangen
Dr. Leila Taher
Tel. 09131/85-48260
Job-Id: 10740
Tel. 09131/85-48260
Medizinisches IK-Zentrum
Herrn Martin Schneider
Krankenhausstraße 12
91054 Erlangen
Published since: 01.07.2025
Job-Id: 10740
Medizinisches IK-Zentrum
Herrn Martin Schneider
Krankenhausstraße 12
91054 Erlangen
Dr. Leila Taher
Tel. 09131/85-48260
Sounds interesting?
Who we are:
CUBiDA is a Core Unit for Bioinformatics, Data Integration and Analysis that acts as a dedicated collaborative partner for biomedical researchers at the Universitätsklinikum Erlangen and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). It also provides training and workshops in bioinformatics. CUBiDA operates within the "IT for Research and Management" (IFM) department, which is a division of the Medical Center for Information and Communication Technology (Medizinisches Zentrum für Informations- und Kommunikationstechnik, MIK) at the Universitätsklinikum Erlangen. MIK provides essential IT support for research, teaching, and management at both the Universitätsklinikum Erlangen and the Faculty of Medicine of the FAU. Furthermore, CUBiDA collaborates closely with the Chair of Medical Informatics at the FAU.
Leila Taher’s research group at the CUBiDA (https://www.mik.uk-erlangen.de/en/ueber-uns/cubida/) is seeking a creative, self-motivated postdoctoral researcher (f/d/m) to investigate the evolutionary constraints shaping the vertebrate non-coding genome.
Using computational and machine learning approaches, you will explore the conservation of genomic spacing between conserved non-coding elements (CNEs), their clustering patterns, and their potential roles in gene regulation and higher-order genome architecture. This project is funded by the Deutsche Forschungsgemeinschaft (DFG, https://www.dfg.de/).
See more in the following selected publications:
- Mau et al. (2023). Elife; 12:e84969. https://pubmed.ncbi.nlm.nih.gov/37432987/
- Almeida da Paz and Taher (2022). Mob. DNA; 13(1):29. https://pubmed.ncbi.nlm.nih.gov/36451223/
- Li et al. Genome (2018). Biol. Evol.; 10(9):2535-2550. https://pubmed.ncbi.nlm.nih.gov/30184074/
Tasks
- Adapt and apply bioinformatics methods to identify and characterize CNEs.
- Reconstruct evolutionary relationships between species to understand the history of CNEs and their spacing.
- Select appropriate publicly available genomic and epigenomic datasets and create workflows to process and analyze them.
- Conduct and execute analyses aimed at identifying significant patterns and correlations that elucidate the association of CNE spacing with CNE function and genome organization.
- Develop and document software, scripts, and workflows used in the analyses.
- Collaborate effectively with other researchers within the research group and with external partners.
- Contribute to the writing of scientific manuscripts for publication in peer-reviewed journals.
- Present research findings at scientific conferences and workshops.
- Potentially contribute to the supervision of undergraduate students and research assistants.
- Maintain and organize research data and computational resources.
Comments
- Submit a single PDF document including a cover letter outlining research interests and motivation, a CV detailing academic background and relevant experience, transcripts and degree certificates, and contact details of two academic referees. Only complete applications will be considered.
This is a 3-year funded position with an immediate start date available. We encourage applications from candidates who can begin as soon as feasible.
Know-how
- Ph.D. in Bioinformatics, Informatics in the Natural Sciences, Molecular Biology or a related discipline with intensive training/experience in bioinformatics
- A strong background in bioinformatics and a keen interest in evolutionary biology
- A proven track record in computational genomics reflected in recent or pending publications
- Proficiency in R, Python or Perl, and Bash
- A solid understanding of statistics
- Strong oral and written communications skills in English
- A clear sense of organization, purpose, and accountability
Additionally advantageous
- Experience with Machine learning