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.

Post-doctoral researcher Digital Twins for the plasma edge in tokamaks (X/F/M)

State-of-the-art simulations of transport effects in the fusion plasma edge has become an import element in the prediction of reactor-scale operational scenarios providing compatibility to both, required heat and particle exhaust constraints and good fusion plasma core performance. Given the multi-scale multi-physics nature of the problem, solutions for a reactor relevant operational regime are hard to achieve given slow numerical convergence rates. An employment of fast numerical tools, that e.g., allow to predict relevant dynamics for plasma control, or allow a full simulation of a discharge by using an integrated approach with suitable fidelity, are not mature yet. In very recent years, the fusion community has started to develop fast surrogate models based on Machine Learning / AI models to speed up significantly the employed tools. Such tools have demonstrated to be generally applicable and to be fast; their predictive capability however is a trade-off balancing speed and level of fidelity.

DIFFER is seeking to hire a post-doctoral researcher for 2 years to work with the Integrated Modeling group at the DIFFER institute in support of EUROfusion projects on AI in fusion exhaust. The work encompasses conduction of plasma edge simulations for reactor design in the EUROfusion DEMO central team for a fusion power plant (FPP) and to develop improvements of fast edge transport model surrogates SOLPS-NN applicable to future fusion reactors such as DEMO. In addition, new hire will collaborate on an additional project related to EUROfusion developments of a digital twin environment (DTE), also in collaboration with EUROfusion public research institutes and DIFFER affiliated private partners.

Business Developer

Als Business Developer ben je primair verantwoordelijk voor het samenbrengen van ons onderzoek, het bedrijfsleven en financiering, naast het opstarten, onderhouden en verbeteren van onze relaties met commerciële partijen. Je speelt een sleutelrol in het opschalen van DIFFER’s onderzoekprogramma’s. Hierdoor vertaal je wetenschappelijke kennis naar maatschappelijke en economische impact.

In de praktijk houdt het in dat je:

• DIFFER’s commerciële netwerk, bestaande uit de industrie en hightech startups, onderhoudt en uitbreidt om nieuwe samenwerkingen tot stand te brengen. Bijvoorbeeld ten behoeve van de realisatie van ons Self-Driving Lab programma, of het ontwikkelen van publiek-private samenwerkingen rondom kernfusie;
• Strategisch advies geeft over marktpotentieel en routes naar toepassing van onderzoek dat wordt uitgevoerd bij DIFFER. Je ontwikkelt businesscases en een valorisatiestrategie voor onze onderzoekslijnen, waarbij het Self-Driving Lab programma momenteel prioriteit heeft;
• In samenwerking met industriële partners nieuwe projectkansen initieert, en DIFFER positioneert in nationale en Europese innovatie-ecosystemen;
• DIFFER vertegenwoordigt op conferenties en in (inter)nationale netwerken zoals Fusion Now, CO2 Value Europe, en communities voor autonome labs.

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

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.

Manager Finance & Control

Als Manager Finance & Control bij DIFFER combineer je financiële eindverantwoordelijkheid met een stevige control functie. Je geeft leiding aan de financiële afdeling, neemt als adviseur deel aan het MT en ondersteunt projectleiders vanuit het onderzoek. Je bent de vraagbaak voor de hele organisatie voor wat betreft het financiële beleid en draagt rechstreeks bij aan het (financiële) succes van het onderzoek dat DIFFER uitvoert.

Concreet houd je je onder meer bezig met:

• Leidinggeven aan de financiële afdeling en zorgdragen voor een positief werkklimaat met aandacht voor de persoonlijk ontwikkeling van het team;
• Regie en coördinatie op de Planning & Control (P&C) cyclus waarbij je analyseert, adviseert en rapporteert over de verschillende P&C producten (begroting, tertiaalrapportages en jaarrekening);
• Toezien op een juiste en volledige gegevensverantwoording voor de bedrijfsvoering door het DIFFER management;
• Opstellen van de financiële rapportages, onder meer in overleg met de overkoepelende onderzoeksinstelling NWO-i;
• Adviseren van het MT en projectleiders vanuit het onderzoek over de financiële voortgang van de onderzoeksprojecten en ervoor zorgen dat deze voldoen aan de subsidievoorwaarden, procedures en wet- en regelgeving;
• Ontwikkelen, inrichten, beheren en bewaken van de structuur en werking van projectcontrol en de bijbehorende (financiële) processen.

Affiniteit met onderzoek en techniek helpt om de financiële uitvoering van projecten te begrijpen. Daarbij volg je nieuwe ontwikkelingen in jouw vakgebied. Als hoofd van de afdeling ben je verantwoordelijk voor de dagelijkse aansturing van 7 collega’s, bestaande uit project control, senior medewerker financiën, crediteuren administratie en inkoop.