Aktiviere Job-Benachrichtigungen per E-Mail!

PhD Student (m/f/d) in Experimental Pediatric Brain Tumor Research

Universitätsklinikum Augsburg A.ö.R.

Augsburg

Vor Ort

EUR 50.000 - 60.000

Vollzeit

Heute
Sei unter den ersten Bewerbenden

Zusammenfassung

A prominent German university hospital is seeking a motivated PhD student to engage in experimental pediatric brain tumor research. The role involves leadership in a liquid biopsy study, characterization of brain tumor cellular compartments, and collaboration within an interdisciplinary team. Ideal candidates hold a Master's in a related field and have experience in molecular biology techniques. This position offers integration into a dynamic academic community with comprehensive support for academic development.

Leistungen

Employee benefits
Integration into clinical and experimental working groups
Support for academic profile development

Qualifikationen

  • Graduated with a Master of Science in Biology, Cancer Biology or Genetics.
  • Experience with various molecular biology techniques.
  • Ability to communicate in German or English.

Aufgaben

  • Lead a Bavarian-wide liquid biopsy study funded by BZKF.
  • Compare methods for measuring tumor burden in CSF.
  • Characterize cellular components via single nucleus transcriptomics.
  • Explore rare pediatric brain tumors' genetic basis.

Kenntnisse

Molecular biology techniques
Programming in Python
Programming in R
Collaboration in interdisciplinary teams

Ausbildung

Master of Science in Biology or related field
Jobbeschreibung

The Children’s Hospital Augsburg(Swabian Children’s Cancer Center) and the University of Augsburg (Biomedical Informatics, Data Mining and Data Analytics) are looking for a PhD student (m/f/d) in experimental pediatric brain tumor research. The position is available from 01.01.2026 and is limited to three years.

In the last years, cutting-edge tumor characterization techniques for tumors have evolved at an unprecedented pace and we have now the opportunity to study tumor composition down to the single cell level. Our group is exploring the genetic hallmarks and evolution of pediatric brain tumors (including rhabdoid tumors and other embryonal tumors).

Recently, we have performed integrative, large-scale multi-omic characterizations of pediatric rhabdoid tumors (Johann et al. Cancer Cell 2016; Erkek, Johann et al, Cancer Cell 2019 Fincke et al. Clin. Can Res. 2023, Mucha et al. Comm Med. 2025) and are now aiming to expand these techniques to other SMARCB1-deficient tumors. Furthermore, to facilitate the translation of the results back to the clinic, another branch of research in our group deals with measuring tumor burden by using liquid biopsies from brain tumor patients. To handle both the experimental and the computational part of this project, we are looking for a highly-motivated PhD student, who is able to work at the interface of molecular biology, epigenetics and computational biology.

As clinic of the University Hospital Augsburg (UKA), we are part of a rapidly growing academic center. The UKA is a large-scale care provider with 1699 patient beds in 22 clinics and approximately 2800 employees. Scientifically, the hospital is member of the NCT-WERA cooperation and thus one location of the NCT (national center for tumor diseases).

Job description

We are looking for a motivated PhD student with a keen interest in pediatric oncology, who is familiar with various basic and state-of-the-art molecular biology techniques (including CRISPR-Cas9, cloning, Western Blotting and others). Knowledge of programing languages such as Python or R is helpful but not mandatory.

The successful applicant will:

  • take the lead in implementing a Bavarian-wide liquid biopsy study (funded by the BZKF, Bavarian Center for Cancer Research)
  • compare different methods to measure tumor burden in the CSF of brain tumor patients
  • characterize the cellular compartment of the CSF by single nucleus transcriptime sequencing
  • take part in other projects seeking to unravel the basis of rare pediatric brain tumors (such as e.g. modeling the impact of DICER1 mutations)
Requirements
  • Graduation (Master of Science) in biology, cancer biology, genetics, bioinformatics or a related field (with demonstrated experience in both bioinformatics and experimental work)
  • Previous wet lab experience during the Masters thesis is helpful
  • Ability to collaborate and communicate within interdisciplinary teams (in German or English)
  • Be available to start the PhD thesis as soon as possible (beginning in early 2026)
What we offer
  • Joint appointment at the University of Augsburg (Prof. Dr. M. Schlesner, Computational Biology) and the University Hospital (Prof. Dr. P. Johann) and integration into both a clinical / experimental and a computational working group
  • The possibility to work on your PhD thesis in a rapidly evolving community
  • The opportunity to learn a wide variety of experimental and computational methods
  • Support in generating an own, academic profile
  • Employee benefits in various companies
  • The grouping takes place in TV-L taking into account your qualifications and personal requirements including the social benefits customary in the public service (company pension, annual special payment)
Contact

For further questions please refer to Prof. Dr. Pascal Johann (Pascal.Johann@uk-augsburg.de).

Application deadline

Please submit your application as soon as possible, latest at the 2nd of November 2026.

We look forward to you!

The position is suitable for people with severe disabilities. Applicants (m/f/d) with severe disabilities will be preferred if they are essentially the same aptitude, ability and professional performance. Women are invited to apply in accordance with Article 7 (3) BayGlG. When starting work at the Augsburg University Hospital, proof of sufficient immune protection in accordance with §§ 23, 23a IfSG must be presented or the same must be met.

Hol dir deinen kostenlosen, vertraulichen Lebenslauf-Check.
eine PDF-, DOC-, DOCX-, ODT- oder PAGES-Datei bis zu 5 MB per Drag & Drop ablegen.