Intern (m/f/d) Computational structural biology and modelling

Looking to gain valuable hands-on experience at KWS?

At KWS, we are advancing data- and knowledge-driven approaches to develop innovative traits for sustainable agriculture. Within our Trait Discovery activities, computational structural biology and advanced protein modelling are important elements to support the nomination and prioritization of trait candidate genes.

Together with colleagues from Trait Discovery and Data Science, you will benchmark computational methods, curate relevant datasets, and translate model outputs into testable hypotheses for experimental validation.

This full-time internship is offered as a fixed-term position for six months and is based at the KWS SAAT SE & Co. KGaA headquarters in Einbeck, Germany.

We're excited to fill it as soon as possible!

Your Key Responsibilities :

• Benchmark, validate and update computational protein modeling platforms using internal and publicly available datasets.
• Run computational workflows on high-performance computing infrastructure and analyze model performance, accuracy, and robustness.
• Document workflows, code, datasets, and results in a reproducible manner, for example in a GitLab repository.
• Present progress and findings in internal project meetings and collaborate closely with experimental and computational scientists.

Bring Your Expertise :

• Qualification in Bioinformatics, Structural Biology, Computer Science, or a related discipline, with experience in machine learning and an interest in foundation models and AI for life sciences
• Technical know-how and hands-on experience in implementation of computational tools in high-performance clusters (e.g., nextflow or snakemake)
• Familiarity with high-performance computing environments and technologies such as SLURM, containers, PyTorch, or Hugging Face is advantageous.
• Experience in applying protein structure-prediction tools like AlphaFold is a plus
• A structured and reliable working style, with a strong focus on documentation, data quality, and reproducibility.
• An open, proactive, and collaborative mindset, combined with the ability to communicate scientific findings effectively.
• Good written and spoken English; German language skills are beneficial.

We look forward to receiving your complete application, including a motivation letter, CV, transcript of records, proof of mandatory internship requirement, and any other relevant certificates.

Please indicate your earliest possible start date in either your motivation letter or CV.

You can find additional internship opportunities in our Job Portal. For general information about internships and thesis collaborations, please visit our FAQ page.