Bachelor thesis - Drought resistance in oats

04.03.2026, Abschlussarbeiten, Bachelor- und Masterarbeiten

Bachelor thesis project: field-based drought tolerance study in oat using an 8-parent MAGIC population (~300 RILs). You will phenotype agronomic and stress traits, support harvest/post-harvest work, and manage/analyse the resulting datasets (June–October) to generate data for downstream GWAS.

The Professorship of Computational Plant Biology at the Technical University of Munich investigates the genetic basis of agronomically important crop traits using bioinformatics. Our research aims to understand plant adaptation to environmental stress and to support the development of resilient crop varieties for sustainable agriculture. Project overview In this thesis project, you will contribute to a field-based study on drought tolerance in oat (Avena sativa) using a multi-parent advanced generation inter-cross (MAGIC) population consisting of eight founder lines and approximately 300 recombinant inbred lines (RILs). The population will be evaluated in field trials under drought-relevant conditions to characterize key agronomic and stress-related traits. You will perform detailed phenotypic assessments pre- and post-harvest and analyse the resulting data using statistical and quantitative methods. The phenotypic data generated in this project will form the basis for subsequent genome-wide association studies (GWAS) aimed at identifying genomic regions associated with drought response and performance. Your responsibilities •Phenotyping of agronomic traits at the end of the growing season (June – July) •Support during harvest and post-harvest processing (July – August) •Measurement of yield and quality traits after harvesting (August – September) •Data management and statistical analysis of phenotypic datasets (September – October) Who we are looking for •Bachelor’s student in Agricultural Sciences and Horticultural Sciences, Life Sciences Biology or a related field •Strong interest in plant breeding, genetics, and field-based research •Previous experience with statistical analysis of agronomic data •Motivation to work independently and reliably in both field and data-analysis tasks What you will gain •Hands-on experience in field phenotyping and experimental plant breeding •Training in quantitative analysis of complex traits •Insight into drought stress biology and resilience breeding in cereals •Experience contributing data to a larger genetic mapping project How to apply If you are interested, please send an email including a short motivation explaining why this project interests you, along with your CV and transcripts of records, by March 31 2026 to Kilian Pfatrisch (kilian.pfatrisch@tum.de).

Kontakt: Prof. Dr. Nadia Kamal, n.kamal@tum.de