"PhD in Machine Learning for Materials Discovery in Self-Driving labs"

Job Description

Accelerating the discovery of clean energy materials requires integrating experimental research with machine learning. Self-Driving Laboratories (SDLs) are emerging research environments where experiments are planned, executed, and analyzed in closed-loop workflows that combine automated experimentation with AI-driven decision-making. At DIFFER, in close collaboration with our external partners, we are developing an SDL dedicated to accelerated discovery of functional energy materials.

In this PhD project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets will include measurements obtained from automated synthesis and optical/electrical characterization workflows. You will be embedded in the AMD research group, and work closely with experimental collaborators to ensure that model development aligns with data quality, measurement conditions, and evolving research priorities.

The SDL operates as a closed-loop system in which each experiment informs the next. Your models will first be used to analyze completed experiments and identify trends, and later integrated into active learning and Bayesian optimization frameworks to suggest which experiments should be performed next. Through this integration, your work will directly shape the experimental strategy of the SDL and accelerate the discovery of new materials.

This position offers a unique opportunity to conduct research at the interface of machine learning, materials science, and autonomous experimentation, contributing to the development of next-generation approaches for data-driven clean energy research.

Responsibilities

• Develop and implement machine learning models to analyze and predict materials properties and performance trends from high-throughput experimental data.
• Design and evaluate feature engineering and data representation strategies for heterogeneous datasets obtained from material synthesis, characterization, and functional testing.
• Apply uncertainty-aware modeling, active learning, and Bayesian optimization approaches to guide experiment selection and support closed-loop decision-making in the SDL.
• Work closely with collaborators to align model development with measurement workflows, data availability, and evolving experimental priorities.
• Ensure reproducible and well-documented analysis practices and contribute to FAIR-aligned data interpretation.
• Explore advanced model families (e.g., generative models or graph/equivariant neural networks) to accelerate candidate discovery and hypothesis generation.
• Disseminate research findings through publications, conference presentations, and consortium meetings.
• Supervise junior student projects where appropriate.
• Complete and defend a PhD thesis within four years.

Requirements

• You have Master’s degree in machine learning, computer science, chemistry, chemical engineering, or materials science, with demonstrated experience at the interface of machine learning and chemical or materials data.
• You have expertise with machine learning methods for scientific data, including regression, classification, representation learning (e.g., spectra, micrographs, or time-series), and uncertainty-aware modelling for experiment planning.
• You are proficient in Python and have experience with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and scientific computing libraries (e.g., NumPy, pandas).
• You have practical experience in data preprocessing, feature engineering, and constructing representations for heterogeneous materials datasets, including handling scientific data formats and database systems (e.g., HDF5, xarray, SQL/NoSQL).
• You are familiair with, or willingness to learn, active learning and Bayesian optimization for guiding experiment selection and closed-loop SDL workflows.
• You have experience with advanced model families such as generative models (e.g., VAEs, GANs, diffusion models) or graph/equivariant neural networks for materials (e.g., CGCNN, SchNet, NequIP), and related tooling (e.g., PyTorch Geometric, pymatgen, ASE) is beneficial but not required.
• You have knowledge of FAIR data principles and experience managing simulation or experimental data in a reproducible and well-structured manner is desirable.
• You have experience with scientific LLM workflows (e.g., prompting or retrieval-augmented search over ELN/LIMS systems) is welcome, but not required.
• You have strong analytical and problem-solving skills, and the ability to work effectively in a collaborative, multidisciplinary research environment.
• You are proficient in English, both in written and spoken.

Conditions of Employment

This position is for 1 FTE, will be for a period of 4 years and is graded in pay scale PhD. The starting salary is € 3024,- in the first year and will increase to € 3873,- in the fourth year of the employment. The position will be based at DIFFER (www.differ.nl) and the working location will be at TU Eindhoven. When fulfilling a position at DIFFER, you will have an employee status at NWO. You can participate in all the employee benefits NWO offers. We have a number of regulations that support employees in finding a good work-life balance. At DIFFER we believe that a workforce diverse in gender, age and cultural background is key to performing excellent research. We therefore strongly encourage everyone to apply. More information on working at NWO can be found at the NWO website (https://www.nwo-i.nl/en/working-at-nwo-i/jobsatnwoi/).

Employer

Dutch Institute for Fundamental Energy Research

DIFFER: Science for future energy

At the Dutch Institute for Fundamental Energy Research (DIFFER) we work on a future in which clean energy will be available to everybody, anywhere in the world. DIFFER’s mission is to perform leading fundamental research on materials, processes, and systems for a global sustainable energy infrastructure.

Our research focuses on two major energy themes: fusion energy as a clean, safe and sustainable energy source and chemical energy. We work in close partnership with (inter)national academia and industry. DIFFER is one of the ten research institutes of the Dutch Research Council (NWO).

Within our institute physicists, chemists, engineers, and other specialists work together in multidisciplinary teams to accelerate the transition to a sustainable society. DIFFER’s workforce is currently composed of ~160 scientists (of which 60 guests and interns), supported by ~40 technicians and ~40 support staff members.

The global nature of the energy challenge is apparent from the international representation of our employees, who originate from over 30 different countries. To strengthen our commitment to diversity, we formed a task force to design, implement, and monitor diversity and gender equality initiatives.

DIFFER is located on the campus of Eindhoven University of Technology.

Additional Information

The closing date of this position is 31 December 2025. First interviews will take place in week 1 and 2 of January 2026.

To apply, please submit your application via the DIFFER online portal. Your application should include the following documents:

• A cover letter explaining your motivation and suitability for the position.
• A curriculum vitae (CV), including a list of publications (if applicable).

Please note: Only complete applications submitted through the online portal will be considered. Applications sent by email will not be accepted.