Postdoc: Environment-Responsive Antibody Design with ML

Postdoc in AI-driven design of environment-responsive antibodies and nanobodies - DTU Bioengineering; Kongens Lyngby; On-site; DKK 420,000 - 540,000; Full time

Job summary

DTU BioEngineering seeks a postdoctoral researcher to advance machine learning-driven design of environment-responsive antibodies and nanobodies. You will bridge AI and protein engineering to computationally design binding proteins whose activity changes with environmental cues, collaborating with wet-lab scientists and international partners.

You will lead the development of models and contribute to translating computational findings into experimental validation, with potential applications in

Qualifications

• PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a related field.
• Strong theoretical and practical experience in deep learning.
• Hands-on experience developing generative models.
• Proficiency in Python, PyTorch and/or JAX.
• Experience training large-scale neural networks on HPC or GPU clusters.
• Experience with representation learning and sequence/structural modeling.
• Strong publication record in AI/ML or interdisciplinary venues.
• Ability to work independently and mentor others.

Responsibilities

• Designing and implementing state-of-the-art deep learning architectures for binding proteins that sense their biochemical environment.
• Developing and optimizing generative models for de novo design of environment-responsive binding proteins.
• Collaborating closely with wet lab scientists to Express and test designs.
• Publishing results and contributing to grant applications.
• Scientific project management and co-leadership.

Skills

Deep learning

Generative models

Python

PyTorch

JAX

HPC

Sequence modeling

Graph neural networks

Education

PhD in Computer Science / ML / Computational Biology

Tools

ESM

ProtT5

AntiBERTy

AbLang2

Geometric DL

Job description

DTU BioEngineering seeks a postdoctoral researcher to advance machine learning-driven design of environment-responsive antibodies and nanobodies. You will bridge AI and protein engineering to computationally design binding proteins whose activity changes with environmental cues, collaborating with wet-lab scientists and international partners.

You will lead the development of models and contribute to translating computational findings into experimental validation, with potential applications in