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Technical University of Denmark

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DTU PhD Scholarship

PhD Scholarship in Generative Molecular Dynamics Modelling of Ion Transport in Designed Proteins and Peptides Technical University of Denmark in Denmark

Degree Level

PhD

Field of study

Computer Science

Funding

This is a full-time, three-year PhD scholarship and employment appointment at DTU. Salary and appointment terms are based on the collective agreement with the Danish Confederation of Professional Associations; the specific salary amount is not stated.

Deadline

Dec 15, 2026

Country flag

Country

Denmark

University

Technical University of Denmark

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Keywords

Computer Science
Chemistry
Materials Science
Biology
Computational Chemistry
Molecular Dynamics
Ion Transport
Sustainable Materials
Physics
Biomaterials
Solid-state Battery
ML

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About this position

The Technical University of Denmark (DTU) is offering a PhD scholarship within the BRIGHT programme, which develops protein- and peptide-based biomaterials as sustainable alternatives to fluorinated PFAS polymers. The initial application area is solid-state battery components, where the project addresses the need for materials with defined composition, programmable chemistry, controlled assembly, stability, processability, and performance.

The PhD researcher will use and extend generative molecular dynamics surrogates, such as PLaTITO, to predict the long-timescale behaviour of designed proteins and peptides. The research will focus on conformational persistence, ion transport, interphase stability, and other dynamic properties relevant to solid-state battery materials. Because the computational model is fast, the project will enable high-throughput screening of large candidate libraries and ranking of sequences for experimental validation.

A further objective is to improve the model for under-represented systems, including cross-linked and non-canonical proteins and peptides. The work combines machine learning, molecular simulation, computational chemistry, protein and peptide design, and biomaterials research, with potential relevance to sustainable materials for energy technologies, coatings, electronics, and other high-value applications.

The researcher will collaborate with scientists across BRIGHT’s dry and wet laboratories and with local, national, and international research networks. Experimental validation will connect computational predictions with protein and peptide design, non-canonical amino acid chemistry, cell-free prototyping, fermentation, purification, materials processing, and structure–function characterization. The broader BRIGHT environment links molecular design with biomanufacturing and material testing, with attention to sustainability, manufacturability, cost, and industrial translation.

The position is based at DTU in Kgs. Lyngby, Denmark, within an international technical-university research environment. The PhD researcher will also contribute to scientific publications, conference presentations, public engagement, social media dissemination, and teaching or supervision of MSc and BSc students and research assistants.

The project is part of BRIGHT, the Novo Nordisk Foundation Biotechnology Research Institute for the Green Transition, a DTU-based initiative focused on sustainable materials, microbial foods, and microorganisms for net-zero agriculture.

Funding details

This is a full-time, three-year PhD scholarship and employment appointment at DTU. Salary and appointment terms are based on the collective agreement with the Danish Confederation of Professional Associations; the specific salary amount is not stated.

What's required

Applicants must hold a two-year master's degree (120 ECTS) or an equivalent qualification at an academic level equivalent to a two-year master's degree, in machine learning, computational physics or chemistry, computer science, or a related field. Applicants should have strong Python programming skills, excellent written and spoken English, strong communication and social skills for interdisciplinary teamwork, analytical ability, and the capacity to work independently. Preferred but non-mandatory qualifications include experience with PyTorch or other deep learning frameworks, molecular dynamics or generative models, an interest in applying machine learning to molecular systems, and familiarity with non-canonical amino acid chemistry. Applicants may apply before receiving the master's degree but must obtain it before starting.

How to apply

Submit the complete online application through the Apply now portal by 15 December 2026 at 23:59 Danish time. Attach one PDF in English containing a motivation letter, CV, grade transcripts and BSc/MSc diploma with the official grading-scale description, and one reference letter with the referee’s email and phone number.

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