Scientist - AI & ML for Protein and Antibody Engineering (m/f/d)

Your Profile:

• PhD in Computational Biology, Protein Engineering, Machine Learning, Computer Science, or a related discipline.
  
• Expert-level understanding of protein and antibody structure-function relationships, enabling rational design and engineering decisions grounded in structural, biophysical, and functional principles.
  
• Strong hands-on experience in machine learning and deep learning, with the ability to independently develop, train, and evaluate models.
  
• Proven ability to work at both application and implementation levels, including adapting state-of-the-art models to drug discovery problems and modifying architectures or training strategies as needed.
  
• Advanced programming expertise in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX, applied to protein/antibody design.
  
• Strong scientific track record, including first-author publications in high-impact journals or conferences.
  
• Excellent communication skills and a collaborative mindset, with the ability to bridge computational and experimental disciplines.
  
• Experience with molecular dynamics simulations is a plus.

Your Tasks:

• Apply novel Machine Learning and Computational Biology methods to address complex research questions in Large Molecule Drug Discovery (LMDD), with a focus on protein and antibodies
  
• Develop, implement and deploy discriminative and generative models (e.g., diffusion models, LLMs) to predict structure, affinity, function of proteins
  
• Collaborate closely with cross-functional teams to integrate and clean biological data from heterogeneous sources.
  
• Design and engineer new de novo proteins and antibodies using state-of-the-art computational approaches for drug development purposes.
  
• Collaborate closely with cross-functional teams to integrate, curate, and analyze biological data from heterogeneous sources.
  
• Facilitate collaboration between experimental scientists and computational experts to maximize the impact of data and model sharing.
  
• Contribute to scientific innovation through publications, internal reports, and presentations at internal and external scientific venues.

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