Postdoc in AI-driven design of environment-responsive antibodies and nanobodies - DTU Bioengineering

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

Are you interested in using machine learning to design proteins that respond to their environment and control biological function with high precision?

We are looking for a postdoc for a 4-year position starting 1st of September 2026 (or soon thereafter) in the Antibody Technologies Group at DTU Bioengineering, led by Professor & Head of Section Andreas Hougaard Laustsen-Kiel.

In this project, you will work at the interface of machine learning and protein engineering, computationally designing antibodies, nanobodies, and binding proteins with condition-dependent activity. You will develop deep learning and generative models for binder design, work closely with wet lab scientists who express and test your designs, and collaborate with leading international partners. The position offers the opportunity to take ownership of your project and contribute to new strategies for controlling protein function in complex biological environments.

The research project: PROTÆCT

This postdoc position is part of the Villum Foundation funded project, PROTÆCT: Precision Regulation Of Toxin Activity through Environmentally-Controlled Targeting, which aims to uncover how protein-based toxins can be controlled using binding proteins that respond to environmental cues such as pH and metal ions.

Most engineered proteins function under static conditions, whereas biological systems are dynamic. PROTÆCT addresses this by developing binding proteins with condition-dependent activity, enabling selective modulation of protein function in defined microenvironments.

You will computationally design antibodies and nanobodies whose binding changes with environmental conditions. This could e.g. enable the development of proteins that are only active in specific tissue environments or are activated when conditions change during a fermentation process, with potential applications in targeted therapeutics, biosensing, and synthetic biology.

The project integrates computational design with advanced in vitro selection technologies. You will play a central role in developing the computational models and in linking these to how environmental factors influence protein–protein interactions.

The research group

You will join the Antibody Technologies Group at DTU Bioengineering, working at the interface of toxinology, antibody engineering, and protein science. The group has a strong track record in developing recombinant antivenoms, environment-responsive antibodies, and translational biotechnological solutions.

We are an international and interdisciplinary team combining experimental and computational expertise. You will be expected to take ownership of your project and work independently, while contributing to a collaborative environment where ideas and expertise are shared across the team.

The group offers a setting with strong academic and industrial connections, providing opportunities to engage with leading collaborators and contribute to research with both fundamental and translational impact.

Responsibilities and qualifications

The postdoc project will focus on in silico design, discovery, and characterization of environment-sensing binding proteins targeting natural toxins.

Key tasks may include:

• Designing and implementing state-of-the-art deep learning architectures for generating binding proteins that sense their biochemical environment
• Developing and optimizing generative models for de novo design of binding proteins that sense their biochemical environment
• Collaborating closely with wet lab scientists who will express and test the designs that have been created
• Publishing results in impactful journals and contributing to future grant applications
• Scientific project management and co-leadership
• Mentoring and training PhD students and MSc/BSc students

We are looking for a candidate who:

• Holds a PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field
• Has strong theoretical and practical experience in deep learning
• Has hands‑on experience developing generative models
• Is highly proficient in Python, PyTorch and/or JAX
• Has experience training large-scale neural networks on HPC or GPU clusters
• Has experience with representation learning and sequence or structural modeling
• Has a strong publication record in relevant AI/ML venues or interdisciplinary journals
• Demonstrates the ability to work independently and drive technical innovation
• Demonstrates the ability to manage projects and mentor students

It will be considered an advantage if you have:

• Experience with protein language models (e.g., ESM, ProtT5) and/or antibody specific language models (e.g., AntiBERTy, AbLang2)
• Experience with structure prediction frameworks, geometric deep learning, or graph neural networks
• Familiarity with computational antibody/binder discovery or molecular modeling

The candidate will collaborate closely with other postdocs and PhD students as well as our international collaborators (particularly University of Porto and the Scripps Research Institute) to strengthen computational and interdisciplinary skills.

As a formal qualification, you must hold a PhD degree (or equivalent).

Assessment

The assessment of applicants will be made by Professor & Head of Section Andreas Hougaard Laustsen-Kiel together with relevant colleagues, group members, and/or project partners.

We offer

DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and terms of employment

The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. In addition, supplements can be negotiated in accordance with DTU’s salary structure for scientific staff: www.inside.dtu.dk/en/human-resources/during-employment/salary/salary-structures.

The period of employment is 4 years. Desired starting date is 1 February 2027. The position is a full‑time position.

You can read more about career paths at DTU here.

Further information

Further information may be obtained from Professor & Head of Section Andreas Hougaard Laustsen-Kiel ().

You can read more at www.bioengineering.dtu.dk

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.