Postdoc in AI-driven design of environment-responsive antibodies
Develop deep learning and generative models for environment-responsive antibodies and nanobodies at DTU Bioengineering.
Best for:Best for machine learning researchers and computational biologists with a strong background in deep learning, generative modeling, and protein science.
Overview
The Technical University of Denmark (DTU) is inviting applications for a four-year postdoctoral position in the Antibody Technologies Group at DTU Bioengineering. Working on the Villum Foundation-funded project PROTÆCT, the successful candidate will focus on the computational design of antibodies and nanobodies with condition-dependent activity. The role bridges machine learning and protein engineering, involving close collaboration with wet-lab scientists and international partners. The applicant is expected to take project ownership, publish impactful research, and mentor students.
What you'll do
- Design and implement advanced deep learning architectures for biochemical environment-sensing binding proteins
- Develop and optimize generative models dedicated to de novo protein binder design
- Collaborate closely with wet-lab researchers to express, test, and validate computational designs
- Publish scientific findings in high-impact international journals and contribute to grant proposals
- Manage scientific projects and provide co-leadership within the research team
- Mentor and train PhD, MSc, and BSc students
Requirements
Degree required: PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field
Language: English; excellent written and spoken proficiency
Instruction: English
Publications: Strong publication record in relevant AI/ML or interdisciplinary journals
- Strong theoretical and practical experience in deep learning
- Hands-on experience in developing generative models
- High proficiency in Python, PyTorch, and/or JAX
- Experience training large-scale neural networks on high-performance computing or GPU clusters
- Experience with representation learning and sequence or structural modeling
- Strong publication record in relevant AI/ML or interdisciplinary venues
- Demonstrated ability to work independently, manage projects, and mentor students
- Must hold a PhD degree or equivalent by the start of the appointment
Funding & terms
How to apply
Deadline: 2026-11-03 (Europe/Copenhagen)
Candidates must submit a complete online application through the official DTU recruitment portal by the specified deadline. All application materials must be combined into a single PDF file written in English, including a cover letter, CV, academic diplomas, and a publication list.
- Cover letter
- CV
- Academic Diplomas
- List of publications
Contact
Andreas Hougaard Laustsen-Kiel· Professor & Head of Section
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