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

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

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. Little is known about crop proteomes – the entirety of proteins that execute and control nearly every aspect of life – and the program aims to fill this gap.

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 master challenging roles, take on substantial responsibility and acquire transferable skills.
2. To conduct studies with the proteome atlas of the 100 most important crop plants for human nutrition, enabling an interdisciplinary project with leading expertise in plant science, proteomics and bioinformatics.

The project: Mapping how proteins form and remodel their interaction networks is essential for deciphering the dynamic proteome. The doctoral candidate 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 will feed into protein function classification, shedding light on the uncharacterized and dark proteome. The candidate will also integrate post‑translational modification information to complement the network and guide strategies to enhance crop resilience and productivity. Additionally, the candidate will actively participate in the maintenance and further development of ProteomicsDB related to the graduate college.

Requirements

• Master’s degree in Data Engineering, Data Science, Bioinformatics, Informatics, or a related discipline.
• Theoretical knowledge and practical skills in statistical analysis, data mining, data integration, machine learning, programming, backend or frontend development, and database design.
• Sound understanding of proteomic technologies and basic biological and (bio)chemical concepts.
• Interest in proteomics research using mass spectrometry.
• Self‑motivated, high potential, strong sense of responsibility and ability to work in a fast‑paced environment on multiple projects.
• Good inter‑cultural and inter‑personal communication skills and ability to present in English.
• Applicants from the top 10 % of their academic year are preferred.

Our offer

You will join a young, highly motivated team of interdisciplinary bioinformaticians who use cutting‑edge proteomic approaches. The position is for four years (10/26 – 09/30). Remuneration is in accordance with TV‑L E13. The TUM offers a stimulating work environment and excellent future perspectives.

Equal opportunity

TUM aims to increase the proportion of women and expressly welcomes applications from women. The position is 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 submit a dossier containing a motivational statement (max. one page), a curriculum vitae, copies of degrees and transcripts, and names and email addresses of at least one referee as a single PDF document no later than 30 May 2026 to Prof. Mathias Wilhelm ( ) and copy Armin Soleymaniniya ( ). Online interviews will be conducted with selected candidates; shortlisted candidates may be invited to the TUM campus in Freising, Germany. Successful candidates are required to start no earlier than 1 October 2026 but latest by 1 April 2027.

Data Protection Information

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