Postdoc in AI-Driven Antibody & Nanobody Discovery at DTU, Denmark

Summary

The Antibody Technologies Group at DTU Bioengineering is seeking a driven Postdoctoral Researcher for a 4-year position under the Villum Foundation–funded PROTÆCT project (Precision Regulation Of Toxin Activity through Environmentally-Controlled Targeting). The role focuses on the in silico design, discovery, and characterization of binding proteins (antibodies, nanobodies) that dynamically respond to environmental cues such as pH and metal ions. You will combine generative deep learning architectures with wet-lab experimental data to selectively modulate protein function in complex biological environments.

Postdoc in AI-Driven Antibody & Nanobody Discovery at DTU, Denmark

Designation

• Postdoctoral Researcher (Postdoc)

Job Overview Table

Research Area

• Computational Biology & Machine Learning for Protein Design
• Toxinology & Antibody/Nanobody Discovery
• Generative Deep Learning (Protein & Antibody Language Models)
• Environmentally Responsive Protein Engineering

Location

• Address: Søltofts Plads, Kgs. Lyngby, 2800, Denmark
• Department: Department of Biotechnology and Biomedicine (DTU Bioengineering), DTU Lyngby Campus

Eligibility / Qualification

Required Qualifications:

• Holds a PhD degree (or equivalent) in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field.
• Strong theoretical and practical background in deep learning and experience developing generative models.
• High proficiency in Python, PyTorch, and/or JAX.
• Hands-on experience training large-scale neural networks on HPC or GPU clusters.
• Experience with representation learning and sequence or structural modeling.
• Strong publication record in relevant AI/ML venues or interdisciplinary journals.
• Proven ability to work independently, drive technical innovation, manage projects, and mentor students.

Desirable / Advantageous Experience:

• Experience with protein language models (e.g., ESM, ProtT5) or antibody-specific language models (e.g., AntiBERTy, AbLang2).
• Structure prediction frameworks and geometric deep learning / Graph Neural Networks (GNNs).
• Familiarity with computational binder discovery, molecular modeling, MLOps, reproducible ML pipelines, or scalable AI infrastructure.

Job Description

Key Responsibilities:

• Model Development: Design and implement state-of-the-art deep learning architectures and generative models for the de novo design of environment-responsive binding proteins.
• Interdisciplinary Collaboration: Work closely with wet-lab scientists who will express, test, and validate generated designs.
• Project Leadership & Mentorship: Scientific project management, co-leadership, and training/mentoring of PhD, MSc, and BSc students.
• Publishing & Grants: Write high-impact research papers and contribute to future grant proposals.
• International Collaboration: Engage with key international partners, including the University of Porto and the Scripps Research Institute.

How to Apply

Interested candidates must submit their application online through the DTU job portal by clicking “Apply Now”.

All application materials must be in English and submitted together in ONE single PDF file containing:

1. Cover Letter (Application)
2. Curriculum Vitae (CV)
3. Academic Diplomas (MSc / PhD degrees in English)
4. Complete List of Publications

Last Date for Apply

• 31 August 2026 at 23:59 Danish Time

Link