PhD in Machine Learning for Autonomous Materials Discovery at DTU Energy and CAPeX Technical University of Denmark in Denmark

Join a dynamic research group at the Technical University of Denmark (DTU) and the Pioneer Center CAPeX, dedicated to revolutionizing materials discovery through predictive design and advanced computational methods. This PhD position offers the opportunity to work at the intersection of machine learning, computational chemistry, and high-throughput experimentation, with a focus on developing new methodologies that extend the capabilities of traditional simulations.

As a PhD candidate, you will develop synergistic workflows to model dynamic, multi-phase systems, enhancing machine learning interatomic potentials and creating ML approaches for rare events and complex environments. A central challenge will be to devise algorithms that automatically bias simulations without prior mechanistic knowledge, contributing to open-source software and guiding systematic explorations toward targeted outcomes. The project is highly collaborative, integrating theoretical models with large-scale experimental datasets and working closely with domain experts.

You will tackle fundamental challenges in sampling and optimization, such as automating the discovery of transition pathways in high-dimensional potential energy surfaces. The research involves cutting-edge ML methods, including generative models, stochastic sampling, and uncertainty quantification, applied to atomistic systems. The interdisciplinary environment at CAPeX provides access to systematic data from high-throughput experimentation, enabling you to bridge theory and experiment and validate your models in real-world contexts.

The research is highly relevant to sustainable production of hydrogen, liquid fuels, and specialty chemicals, with direct industrial applications. You will be part of the Autonomous Materials Discovery section at DTU Energy, collaborating with around 40 colleagues in computational and experimental science, machine learning, and artificial intelligence. CAPeX unites leading Power2X experts from Danish and international universities, driving breakthroughs in sustainable and scalable materials discovery and development.

DTU Energy is a world-leading department focused on functional materials, components, and systems for sustainable energy technologies, including fuel cells, electrolysis, power-to-x, batteries, and carbon capture. The department emphasizes strong competencies in electrochemistry, atomic-scale and multi-physics modelling, autonomous materials discovery, and materials processing.

Eligibility: Applicants must hold a two-year master's degree (120 ECTS) or equivalent in computer science, chemistry, physics, materials science, or a related field. Experience with deep learning frameworks, atomistic simulations, and quantum chemical calculations is preferred. Interest in software development, automating computational workflows, and fundamental reaction mechanisms is expected. Proficiency in English and willingness to work in an international, interdisciplinary team are required.

Funding: The position is fully funded for 3 years, with salary and benefits according to Danish standards and the collective agreement with the Danish Confederation of Professional Associations.

Application deadline: 30 April 2026. The expected start date is 1 September 2026 or as agreed.

How to apply: Submit your application online via the provided link. Prepare a single PDF file including your cover letter, CV, grade transcripts, and BSc/MSc diploma (with grading scale). Applications must be in English and received before the deadline. For further information, contact Tenure Track Assistant Professor Miguel Steiner or Professor Tejs Vegge.

DTU is committed to diversity and inclusion, and encourages applications from all qualified candidates regardless of background. The university offers a supportive international environment and resources for relocation to Denmark.