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Technical University of Denmark

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PhD scholarship in generative molecular dynamics modelling of ion transport in designed proteins and peptides - BRIGHT Technical University of Denmark in Denmark

Degree Level

PhD

Field of study

Computer Science

Funding

PhD scholarship funded under DTU; appointment based on the collective agreement with the Danish Confederation of Professional Associations. Full-time position for 3 years.

Deadline

Dec 15, 2026

Country flag

Country

Denmark

University

Technical University of Denmark

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Keywords

Computer Science
Chemistry
Electrical Engineering
Chemical Engineering
Materials Science
Deep Learning
Biology
Molecular Dynamics
High-throughput Screening
Physics
Biomaterials
Solid-state Battery
ML

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About this position

This PhD project is part of BRIGHT at the Technical University of Denmark, where researchers are building a general platform for designing protein- and peptide-based biomaterials. The first testbed is solid-state battery components, with the broader aim of replacing fluorinated PFAS polymers with de novo designed proteins and peptides.

The project focuses on predictive modelling of long-timescale behaviour in designed proteins and peptides using generative molecular dynamics. A key goal is to understand conformational persistence and ion transport, so that candidate sequences can be screened computationally and prioritised for experimental validation.

You will work in a highly interdisciplinary environment spanning dry and wet labs, linking computational model development with experimental testing of the biomaterials platform. The role includes improving surrogate models for challenging systems such as cross-linked or non-canonical proteins and peptides, and helping advance a fast screening workflow for large candidate libraries.

BRIGHT’s Mission 1A is centered on novel biomanufacturing-enabled materials, including AI-guided protein and peptide design, microbial engineering, non-canonical amino acid incorporation, cell-free prototyping, fermentation, purification, and materials testing. This PhD sits within that broader effort to develop PFAS-free, sustainable materials with potential relevance beyond batteries.

At DTU, the project is embedded in an international, collaborative research environment with strong links across academic and industrial partners. The position is aligned with a programme that combines molecular design, scalable biomanufacturing, and material characterization for green transition applications.

Funding details

PhD scholarship funded under DTU; appointment based on the collective agreement with the Danish Confederation of Professional Associations. Full-time position for 3 years.

What's required

Applicants must have an MSc in machine learning, computational physics or chemistry, computer science, or a related area, with a two-year master's degree (120 ECTS points) or an equivalent academic level. Strong programming skills in Python are required, along with excellent English proficiency in written and oral form, excellent communication and social skills for interdisciplinary teamwork, strong analytical skills, and the ability to work independently. Preferred but not mandatory: experience with deep learning frameworks such as PyTorch, experience with molecular dynamics or generative models, interest in applying machine learning to molecular systems, and familiarity with non-canonical amino acid chemistry.

How to apply

Apply online via the 'Apply now' link and submit all materials as one PDF in English. Include a cover letter, CV, grade transcripts, BSc/MSc diploma with grading scale description, and one reference letter with the referee’s email and phone number.

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