MSAID is looking for a working student in deep-learning at our office at the university campus in Garching, near Munich or Berlin, Germany.

MSAID is an innovative bioinformatics spin-off from the Technical University of Munich. Our interdisciplinary team specializes in the development of state-of-the-art deep learning and analytics pipelines for high-throughput protein analyses and we are looking for a strong candidate to support our development team.

The precise identification and quantification of proteins is a crucial step in the understanding, diagnosis, and treatment of diseases. Mass spectrometry-based proteomics is a powerful toolbox for the simultaneous analysis of thousands of proteins and represents the go-to technology for biomarker research and drug discovery. However, current data analysis solutions often rely on simple concepts that remained unchanged for decades – a challenge we aim to resolve.

Your contribution

  • You will contribute to MSAID’s research efforts for deep learning in molecular biology
  • You will contribute to the creation of a deep learning-based protein analysis software to aid the understanding, diagnosis, and treatment of diseases
  • You will evaluate and integrate the newest machine learning research findings into MSAID’s internal machine learning pipelines

Your profile

  • Studying Computer Science, Bioinformatics or a related field
  • Demonstrated knowledge of scientific Python (numpy, pandas)
  • Comfortable with Linux and bash
  • Highly motivated, independent learner with curiosity to explore new fields
  • Basic knowledge in machine learning, pattern recognition or data science are a plus

Our offer

  • Opportunity to apply machine learning to biological and medical data making a real impact
  • Environment for continuous professional development
  • Young and highly motivated team with a flat hierarchy
  • Relaxed startup atmosphere
  • Student positions in Munich and Berlin

We look forward to receiving your comprehensive application as a single PDF file including your earliest starting date (to Dr. Martin Frejno):