PhD / PostDoc (m/f/x)
LMU München
- Institution
- LMU München
- Position type
- PhD
- Application deadline
- 28 August 2026
- Location
- München, Bayern, DE
- Funding
- Fully funded position
Want to be the strongest applicant for this position?
Book a free 15-minute call with our team. We will look at your profile against what LMU München is actually looking for, and tell you honestly where you stand and what to strengthen before you apply.
Book a free call about this position →No cost, no obligation. Mentors who have secured funded PhD positions themselves.
We mentor STEM applicants only. If your research aspirations are outside STEM subjects, you're very welcome to use this board and apply directly; we just aren't the right people to mentor you.
About this position
About us:
The StaBLab is part of the Institute of Statistics at LMU Munich. We provide statistical consulting for each step of the data analysis pipeline for research projects as well as for partners in industry.
We are looking for you:
in Munich
Your tasks and responsibilities:
- Develop Bayesian hierarchical models to account for complex structures of uncertainty in experimental and observational studies
- Support in teaching applied statistics courses such as linear modelling
- Manage research assistants on statistical consulting projects
Your qualifications:
- Hold an excellent master’s degree in statistics or a related field (PostDoc applicants must hold a PhD degree in a related field)
- Bring interest (and ideally experience) in Bayesian inference and meta-research
- Program proficiently in R and / or Python
- Demonstrate strong communication skills in English, with German language proficiency considered an advantage
- Balance long-term research project goals with consulting requests independently
- Willing to work in a team
- Bring an interest in biomedical and meta-research questions and a willingness to work across disciplines
Benefits:
Salary and benefits are in accordance with the German public sector scale (TV-L E13, 100%, 59.000 – 73.000 €, TV-L). The position is initially limited to one year, with a planned extension of up to two additional years.
The office is in Munich, working from home is possible, and working hours are flexible. Successful candidates are encouraged to present their research on national and international conferences.
The position is suitable for disabled persons.
Also possible in a part-time capacity.
People with disabilities who are equally as qualified as other applicants will receive preferential treatment.
Contact:
How to apply
Send an E-Mail to Dr. Sabine Hoffmann: kontakt@stablab.stat.uni-muenchen.de with the subject line “Position in Bayesian modelling” and include
- CV
- Academic transcripts (bachelor, master, list of publications)
- If available, academic and / or employment references
Applications are reviewed on a rolling basis; Only shortlisted candidates will be contacted or invited to interview.
Application Deadline: 28.08.2026
Where knowledge is everything.
LMU researchers work at the highest level on the great questions affecting people, society, culture, the environment and technology — supported by experts in administration, IT and tech. Become part of LMU Munich!
In the course of your application for an open position at Ludwig-Maximilians-Universität (LMU) München, you will be required to submit personal information. Please be sure to refer to our LMU Privacy Policy. By submitting your application, you confirm that you have read and understood our data protection guidelines and privacy policy and that you agree to your data being processed in accordance with the selection process.
Apply directly to LMU München
Emerging Scholars Council is not the employer and does not recruit for this position. It is advertised by LMU München, and your application goes to them. We help students prepare and strengthen their applications.
Go to the official application page →Applications close 28 August 2026.
Contact listed in the advert
Some positions — especially in Germany, Austria and Switzerland — are filled by writing directly to the supervisor rather than through an online form. These addresses were taken from the advert text, so check them against the official listing before you write.
A cold email to a supervisor is the highest-leverage thing you can send — and the easiest to get wrong. Ask us to look at yours before you hit send.