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Master Thesis opportunity at University of Tübingen and Computomics

Master Thesis / internship opportunity at the University of Tübingen in collaboration with Computomics

Dr. Franz Baumdicker, Junior Research Group Leader, Mathematical and Computational Population Genetics at the University of Tübingen, is offering a 6-month master thesis (alternately an internship) for a candidate in the field of population genetics and plant breeding.

Computomics has been working with customers on population genetics and plant breeding for several years, and created a groundbreaking machine learning technology for predictive breeding.

The collaboration between the group leader and Computomics began informally. This is the first joint project - and a great opportunity for a master candidate to explore.

Evaluating the effects of recombination on phenotype prediction in simulations of breeding schemes 

Project idea
Breeding new crop variants is an important task, especially in the context of climate change and a growing world population. To increase the efficiency and speed of breeding programmes, simulation and phenotype prediction are useful tools. Great progress has been made in both areas in recent years. Genome evolution can now be simulated very efficiently with msprime, and more complex scenarios that take into account the characteristics of the organism in question are possible with stdpopsim. Modern machine learning methods are used to predict phenotypes MLphenotype. The aim of the master’s thesis is to simulate genomic data with and without recombination events and to investigate the influence on the quality of phenotype prediction.

The master thesis should consider the following points:

  • Simulation of genotypes of different crops resulting from crossing known genomes 
  • Simulation scenarios without, with constant and with variable recombination will be considered 
  • Consideration of different breeding schemes in the simulations 
  • Simulating phenotypes from these genotypes 
  • Predicting different phenotypes and traits using simple machine learning techniques 
  • Evaluating the effect of training data sets that were generated with or without recombination on the prediction performance 
  • Placing the results in a scientific context and writing an academic paper on the results. 

How to apply
Are you interested in the master program, alternately an internship (working student), and you are currently studying at the University of Tübingen? Then we welcome your application. You will be working in close collaboration with Franz Baumdicker and Computomics. The scheduled timeframe is 6 months, beginning as soon as possible. 

Simply get in touch with Franz Baumdicker at 

If you have any questions regarding the project content, feel free to contact Rupashree Dass or Federico Casale

You can also find the offering on the Praxisportal Universität Tübingen

We’re looking forward to working with you on this exciting project!

Image “plant phenotypes” created by DeepAI

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