PhD Fellowships in Theoretical Catalysis
Explore cutting-edge computational methods to model and predict catalytic materials for electrochemical energy conversion.
Best for:Best for recent master’s graduates in chemistry, physics, materials science, or computational science with strong computational modeling skills and a passion for energy materials research.
Overview
The University of Copenhagen’s Department of Chemistry invites applicants for two PhD fellowships in Theoretical Catalysis, embedded within the Theory group of the Center for High Entropy Alloy Catalysis (CHEAC) and the ERC Synergy project DEMI. These positions focus on advancing computational simulations—including Density Functional Theory, machine learning, and statistical modeling—to understand and design high-entropy alloys and electrocatalysts for energy applications like water splitting and CO₂ reduction. The successful candidates will contribute to interdisciplinary research, collaborate with international partners, and conduct independent projects under the supervision of Professor Jan Rossmeisl. The role offers a dynamic environment in a leading European university, with opportunities for international research stays and academic growth.
What you'll do
- Conduct independent research on computational modeling of catalytic materials, focusing on high-entropy alloys and electrocatalyst stability.
- Develop frameworks to predict metastability and activity of electrocatalysts using Density Functional Theory, machine learning, and statistical methods.
- Collaborate with international research groups and participate in interdisciplinary projects within the ERC Synergy project DEMI.
- Complete PhD-level coursework (approx. 30 ECTS) and contribute to teaching or knowledge dissemination activities.
- Publish research findings in high-impact scientific journals and present results at conferences.
- Write and defend a PhD thesis based on original research contributions.
- Engage in international research stays, preferably abroad, to broaden research experience.
Requirements
Degree required: Master’s degree (or equivalent, 180 ECTS BSc + 120 ECTS MSc) in Chemistry, Physics, Materials Science, Nanochemistry, Computational Chemistry, or a related field.
Degree offered: PhD
Language: English proficiency (IELTS 6.5 or equivalent, or proof of prior academic work in English).
Instruction: English
- Proven expertise in computational modeling, particularly Density Functional Theory (DFT).
- Strong background in statistical analysis or machine learning for scientific applications.
- Experience with atomic-scale simulations or materials science research.
- Familiarity with programming languages (e.g., Python, Fortran) for scientific computing.
- Ability to work collaboratively in an interdisciplinary research environment.
- Applicants must have completed a master’s degree (or equivalent) in a relevant scientific field.
- No nationality restrictions, but eligibility for Danish PhD enrolment applies (see [UFM assessment guidelines](https://ufm.dk)).
- No age restrictions, but the position is limited to 3 years (full-time).
Funding & terms
How to apply
Deadline: 2026-10-18 (Europe/Copenhagen)
Applicants must submit a complete application package electronically, including a motivated letter of application, CV, diplomas/transcripts (with English translations if needed), publication list, and reference letters. All documents must be in English. Applications are reviewed by an unbiased expert assessor, and shortlisted candidates will be interviewed in November 2026.
- Motivated letter of application (max. one page)
- Curriculum vitae (including education, experience, language skills, and relevant skills)
- Original diplomas for Master of Science and transcript of records (or certified copies if not completed)
- Publication list (if applicable)
- Reference letters (if available)
Contact
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