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Technical University of Denmark

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PhD Scholarship in Generative Protein and Peptide Design for Sustainable Biomaterials Technical University of Denmark in Denmark

Degree Level

PhD

Field of study

Computer Science

Funding

The position is a full-time, three-year PhD appointment. Salary and appointment terms follow the collective agreement with the Danish Confederation of Professional Associations and DTU's salary structure for scientific staff. The post is described as a PhD scholarship, but no specific stipend amount or tuition-coverage details are provided.

Deadline

Dec 6, 2026

Country flag

Country

Denmark

University

Technical University of Denmark

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Keywords

Computer Science
Chemistry
Biomedical Engineering
Chemical Engineering
Materials Science
Biology
Computational Biology
Active Learning
Protein Engineering
Sustainable Materials
Biomanufacturing
Bioinformatic
Biomaterials
Solid-state Battery
ML

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About this position

Research focus: This PhD project develops generative computational methods for designing protein- and peptide-based biomaterials as sustainable alternatives to fluorinated PFAS polymers. The first application is solid-state battery components, where the project will target materials with adhesion, cohesion, ion conduction, and controlled degradability.

The researcher will combine molecular foundation models with iterative laboratory feedback to generate de novo protein and peptide designs. The work will adapt tools such as RFdiffusion, ProteinMPNN, and AlphaFold-type models, while extending design algorithms to handle natural and non-canonical amino acids and to plan their biosynthesis and biomanufacturing.

A central theme is the development of lab-in-the-loop methods. The researcher will create information-dense synthesis algorithms, generative model-controlled stochastic synthesis strategies, and integrated models that learn from diverse material-property measurements. Experimental data will be incorporated continuously with quantified uncertainty so that each design-build-test-learn cycle yields high-information training data.

The project also addresses expanded genetic codes, enabling non-canonical residues to be encoded and biosynthesised rather than only chemically synthesised. The researcher will work closely with experimentalists to automate and scale up design, synthesis, testing, and learning, and will contribute to shared latent representations for connecting molecular designs with material properties.

The position is part of BRIGHT Mission 1A, the Novo Nordisk Foundation Biotechnology Research Institute for the Green Transition's programme on novel biomanufacturing-enabled materials. The broader research environment spans AI-guided protein and peptide design, microbial engineering, non-canonical amino-acid biosynthesis, cell-free prototyping, fermentation, purification, materials processing, analytical quality control, and structure-function characterization. The programme connects molecular design to scalable biomanufacturing and material testing, with emphasis on sustainability and industrial translation.

Based at the Technical University of Denmark, BRIGHT brings together academic and industrial partners working on sustainable materials and other biosolutions. The PhD researcher will collaborate within the research group and international networks, publish in peer-reviewed journals and conferences, engage in public dissemination, and teach or supervise MSc and BSc students and research assistants.

Funding details

The position is a full-time, three-year PhD appointment. Salary and appointment terms follow the collective agreement with the Danish Confederation of Professional Associations and DTU's salary structure for scientific staff. The post is described as a PhD scholarship, but no specific stipend amount or tuition-coverage details are provided.

What's required

Applicants must hold a two-year master's degree (120 ECTS) or an equivalent degree at the academic level of a two-year master's degree, in computational biology, bioinformatics, biophysics, computer science, or a related field. Applicants should have Python programming experience, experience with deep learning frameworks such as PyTorch, excellent written and spoken English, strong communication and social skills for interdisciplinary teamwork, strong analytical skills, and the ability to work independently. Preferred but non-mandatory qualifications include knowledge of protein structure and molecular biology; experience with RFdiffusion, ProteinMPNN, or AlphaFold; familiarity with DNA-encoded libraries, genetic code expansion, or next-generation sequencing; and interest in multi-objective optimisation, reinforcement learning, and connecting computational design with experimental implementation. Applicants may apply before receiving the master's degree but must obtain it before starting.

How to apply

Submit a complete online application through the DTU Apply Now portal by 6 December 2026 at 23:59 Danish time. Upload one PDF in English containing a motivating letter, CV, grade transcripts and BSc/MSc diploma with the official grading-scale description, and one recommendation letter including the referee's email and phone number.

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