Freising, Germany

Doctoral Candidate f/m/d in computational proteomics/bioinformatics

26.03.2026, Wissenschaftliches Personal

Doctoral Candidate f/m/d in computational proteomics/bioinformatics with a focus on plant proteomics Candidates must hold a master´s degree in Data Engineering, Data Science, Bioinformatics, Informatics or a related discipline. Available 10/26-09/30

The graduate college “The Proteome that Feed the World”:

Plants are the nutritional basis of life on earth and protein-rich foods from plants are a global megatrend essential for sustaining an increasing human population and counteracting climate change. However, little is known about crop proteomes – the entirety of proteins that execute and control nearly every aspect of life. Therefore, TUM pursuits for the second time the visionary doctoral program with high socio-economic relevance on the topic of “The Proteomes that Feed the World” with the aims:

1. To train and develop future leaders in science, industry and society who excel in research, management and communication. This will be achieved by implementing a professional training and project management structure in which doctoral candidates (DCs) master challenging roles, take on substantial responsibility and acquire important transferable skills.

2. To conduct studies with the proteome atlas of the 100 most important crop plants for human nutrition. This enables an interdisciplinary project with leading expertise in plant science, proteomics and bioinformatics. Added value comes from a large international network of excellent academic and industry partners as well as a vibrant local scientific community.

The project:
Mapping how proteins form and remodel their interaction networks is essential for deciphering the dynamic proteome. Our DC will combine co-expression patterns from the crop proteome atlas, new proteomics data, and protein structure predictions to build a graph neural network that predicts condition-specific protein-protein interactions. High-confidence predictions are expected to feed into protein function classification, shedding light on the uncharacterized and dark proteome. We aim to integrate post-translational modification information mined from the crop proteome atlas, newly generated data, and publicly available resources to complement the network. Ultimately, this project will guide strategies to enhance crop resilience and productivity by leveraging condition-specific networks in plant proteomes. In addition, the doctoral candidate will actively participate in the maintenance and further development of ProteomicsDB particularly in relation to all data acquired in the context of the graduate college “The Proteome that Feed the World”.

Requirements:
Candidates must hold a master’s degree in Data Engineering, Data Science, Bioinformatics, Informatics, or a related discipline. Essential skills include theoretical knowledge of and practical skills in statistical analysis, data mining, data integration, machine learning, programming, backend or frontend development, and database design.
Additional desirable skills include a sound understanding of proteomic or related technologies as well as basic biological and (bio)chemical concepts.
Interest in understanding technologies in proteomics research using mass spectrometers is expected.
We are looking for a self-motivated and broadly interested individual with high potential and a strong sense of responsibility.
Flexibility and the ability to work in a fast-paced environment on multiple scientific and infrastructure projects are essential.
Good inter-cultural and inter-personal communication skills as well as the ability to present in English are also important.
We aim to recruit individuals of the highest potential typically from the top 10% of an academic year.

Our offer:
You will join a young and highly motivated team of interdisciplinary bioinformaticians who use the latest proteomic approaches to gain insight into the biological processes that govern life. TUM is one of the best academic institutions in Germany and offers a stimulating work environment and excellent future perspectives. The positions are available for four years (10/26 – 09/30). Remuneration is in accordance with TV-L E13 according to the professional qualification. You will join an interdisciplinary team of life scientists and bioinformaticians who use the latest proteomic approaches and equipment to better understand plant biology. The TUM is one of the best academic institutions in Germany, a well worked out international program, offers a stimulating work environment and excellent future perspectives.

Equal opportunity:
TUM is aiming to increase the proportion of women and, therefore, expressly welcomes applications from women. The position is also suitable for severely disabled persons. Severely disabled applicants will be given preference if their suitability, qualifications and professional performance are otherwise essentially equal.

Application process:
Applicants should send a dossier containing a motivational statement (max. one page), a curriculum vitae summarizing qualifications and experience, copies of degrees and transcripts of study records, names and the email addresses of at least one referee as a single PDF document and no later than May 30th, 2026 to Prof. Mathias Wilhelm (mathias.wilhelm@tum.de) with Armin Soleymaniniya (armin.soleymaniniya@tum.de) in CC. Online interviews will be conducted with selected candidates and remaining shortlisted candidates may be invited to the TUM campus in Freising, Germany. Successful candidates are required to start no earlier than October 1st 2026, but latest by 1st April 2027.

Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the data protection information on collecting and processing personal data contained in your application in accordance with article 13 of the General Data Protection Regulation (GDPR). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: Prof. Mathias Wilhelm: mathias.wilhelm@tum.de , Armin Soleymaniniya: armin.soleymaniniya@tum.de