Enviro-Responsive Antibody Design Postdoc (AI/ML)

DTU — Technical University of Denmark; Kongens Lyngby; On-site; DKK 420,000 - 520,000; Full time

Job summary

DTU Bioengineering's Antibody Technologies Group invites applications for a 4-year postdoc starting 1 September 2026. The project PROTÆCT merges machine learning with protein engineering to design antibodies, nanobodies and binding proteins that respond to environmental cues.

You will develop deep learning models, work with wet-lab partners for validation, and publish results while advancing translational strategies. The role offers independence and strong international collaboration.

Qualifications

• PhD in a relevant field.
• Strong theoretical and practical deep learning experience.
• Hands-on experience with generative models.
• Proficient in Python, PyTorch and/or JAX.
• Experience training large-scale neural networks on HPC/GPU clusters.
• Strong publication record in AI/ML or interdisciplinary venues.
• Ability to work independently and drive innovation.
• Mentor PhD/MSc/BSc students.

Responsibilities

• Design and implement state-of-the-art deep learning architectures for environment-sensing binding proteins.
• Develop generative models for de novo design of binding proteins.
• Collaborate with wet-lab scientists for expression and testing.
• Publish results in impactful journals and contribute to grants.
• Scientific project management and co-leadership.
• Mentor and train PhD and MSc/BSc students.

Skills

Deep learning

Python

PyTorch

JAX

HPC/GPU

Sequence modeling

Publications

Project management

Mentoring

Education

PhD in CS/ML/AI/Computational Biology

Tools

ESM

ProtT5

AntiBERTy

AbLang2

AlphaFold

Graph neural networks

Job description

DTU Bioengineering's Antibody Technologies Group invites applications for a 4-year postdoc starting 1 September 2026. The project PROTÆCT merges machine learning with protein engineering to design antibodies, nanobodies and binding proteins that respond to environmental cues.

You will develop deep learning models, work with wet-lab partners for validation, and publish results while advancing translational strategies. The role offers independence and strong international collaboration.