We are always open to inquiries from dedicated students in the fields of Bioinformatics, Molecular Biotechnology, Biology, and Agricultural Biosciences. Computational skills are especially beneficial for our work, particularly experience with high-performance computing (HPC), command-line tools, and programming in Python or R. If these skills are not yet developed, we typically recommend you to take some courses first to build a solid foundation. Please contact us as early as possible.
 

In particular: 

1. We offer a Bachelor or Master thesis in a project on Drought Stress Response in Oat for a student of Biology or Agricultural Biosciences or similar, jointly supervised by Helmholtz Munich and TUM.

In a large-scale drought stress experiment on oat, we’re working with a broad diversity of genotypes — from wild relatives to landraces and elite cultivars. The student will:

• Quantify stomatal density at multiple drought timepoints (microscopy @Helmholtz)
• Analyze plant architecture using image-based data from a high-throughput phenotyping platform
• Correlate physiological traits (e.g., stomata, yield) with gene expression data (analysis @TUM)

The start is as soon as you are available, Location: Helmholtz Munich & TUM Freising

 

General possible topics for Bachelor/Master theses and internships include:

Comparative Genomics in Cereals: Projects in this area focus on comparative analyses across cereal genomes. Topics include the evolution of paralogs and homeologs, subgenome-specific expression, transcription factor binding site divergence, and pangenome approaches to assess genic and structural variation and regulatory diversity. The aim is to understand how genome evolution shapes gene function and trait expression across cereal species, with applications in breeding and resilience.

Plant-Microbe Interactions: Within the TRR356 framework, we offer projects investigating the genomic basis of plant interactions with microbes—both symbiotic and pathogenic. Students may work on identifying interaction-specific genes across species, or on the comparative analysis of genes involved in different types of plant-microbe relationships. The goal is to uncover genetic mechanisms that determine compatibility, resistance, or beneficial symbiosis, contributing to future strategies for crop protection and sustainable agriculture. 

Transcriptome and Network Analyses: These projects investigate gene regulation using RNA-seq data across tissues, conditions, or ploidy levels. Topics include differential expression analysis, co-expression network construction, and comparative transcriptomics across species or genotypes. An example is analyzing how polyploidization shapes gene expression patterns in oat. This work helps us understand how regulatory networks adapt to genome complexity, stress, and development – a key for systems-level insights into crop traits.

Gene Family Evolution and Functional Diversification: This includes projects on the expansion, contraction, and structural diversification of gene families linked to traits such as drought tolerance, nutrient use efficiency, or cell wall composition. Students can for example focus on well-characterized gene families (e.g. β-glucan biosynthesis, stress-related genes) or specific traits. The aim is to uncover evolutionary innovations and functional shifts that underlie agronomic traits, helping to identify promising targets for improvement.

Structural Variation and Trait Associations: These projects aim to detect and interpret SNPs and structural variants in pangenomes and relate them to agriculturally relevant traits. Analyses may involve diversity panels, wild-domesticated comparisons, or graph-genome approaches to capture hidden variation (still quite exploratory). This research supports the discovery of genomic regions and variants linked to key traits, with potential for marker development and precision breeding.

Students are also welcome to propose their own project ideas within our thematic focus areas. All projects combine strong biological questions with computational analysis and offer the opportunity to work on real, unpublished data in an active research environment.