PhD in Data-Driven Molecular Discovery for Energy Storage

The PhD project focuses on the computational discovery and optimisation of organic redox-active molecules for next-generation aqueous redox flow battery and electrochemical booster systems. Aqueous redox flow batteries are promising candidates for long[1]duration energy storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine-learning (ML)- driven and physics-based computational workflows to screen large molecular libraries, predict key electrochemical and physicochemical properties, and deliver ranked shortlists of high[1]performance, cost-effective energy storage molecules and mediators. The work includes molecular property prediction, stability assessment, and matching candidate molecules to relevant electrochemical operating windows and electrolyte environments. This PhD is part of the national Redox Blend consortium, providing computational insights that directly guide experimental synthesis and validation across partner institutions.

You will be embedded in the AMD research group at DIFFER and work closely with external experimental collaborators to ensure alignment between computational models, data quality, and experimental conditions.

Responsibilities:

• Develop and extend ML and physics-based workflows, such as RedCat, for automated molecular screening for energy storage in aqueous redox flow batteries.
• Perform high-throughput DFT and MD calculations to validate and refine top-ranked molecular candidates.
• Deliver ranked shortlists and detailed property reports to guide experimental synthesis and testing.
• Work closely with project collaborators to align computational model development and data availability with evolving experimental measurement workflows.
• 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.

To get started in this role at DIFFER, it is important you have:

• A Master’s degree in computational chemistry, chemical engineering, materials science, or a closely related field, with demonstrated interest or experience at the interface of computation and data driven methods.
• Experience with computational modelling techniques relevant to molecular and/or materials discovery, such as DFT, MD, and ML–based property prediction.
• Basic knowledge of physical chemistry, thermodynamics, or electrochemistry.
• Proficiency in Python and experience with scientific computing and data-analysis libraries, and familiarity with ML frameworks for molecular and materials studies.
• Experience with reproducible research practices, structured data management, and FAIR data principles is considered an advantage.

At DIFFER we believe that a workforce diverse in gender, age, and cultural background is key to performing excellent research. We offer you a job in an environment that is relevant to society, in the heart of the hightech Brainport region.

We offer you excellent employment conditions:

• An employee contract for 1 FTE, for a period of 4 years according to scale 19 number 1, starting with a monthly salary of € 3.115,- at the beginning of the Phd contract and ending with € 3.989,- in the last year.
• Excellent secondary employment conditions according the collective labour agreement for the research institutions (CAO-OI). This means you will receive a holiday allowance of 8% and an end-of year bonus of 8.33%. You will participate in the pension scheme run by the Algemeen Burgerlijk Pensioenfonds (ABP).
• With a fulltime contract you will be working 40 hours per week with a formal working week of 38 hours, and you have the right to 338 hours of leave per year.

The position is available in the Autonomous Energy Materials Discovery (AMD) group of the Chemical Energy (CE) department at DIFFER.

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 ~4 0 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.