Postdoc in Machine Learning for Energy Materials

Do you want to develop machine learning methods that help uncover how materials are formed and guide the next experiment? If you are building a career as a researcher in machine learning and are passionate about developing methods for scientific discovery,

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an excellent opportunity to advance your research. We are seeking a highly motivated postdoctoral researcher to join DTU Compute and develop probabilistic modeling and active learning methods for next-generation solar energy materials. The position is funded by a Villum Synergy grant within the project “Data-driven design of tunable chalcogenide thin films for next-generation indoor and tandem photovoltaics.” You will join an interdisciplinary collaboration between DTU Compute and the Thin-Film Photovoltaics Group at DTU Electro, working closely with a dedicated experimental postdoc. Project description How can we learn from limited experimental data to understand materials synthesis and decide which experiment to perform next? This project addresses this question for antimony-based chalcogenide thin films for indoor and tandem solar cells. Their properties depend on complex interactions between synthesis conditions, which influence crystal structure, defect formation, and ultimately photovoltaic performance. You will develop probabilistic models that connect synthesis conditions with material properties and device performance, combining experimental data with physical and chemical knowledge. Relevant approaches include Gaussian processes, Bayesian neural networks, and uncertainty-aware representation learning. Alongside accurate prediction, a central aim is to identify informative descriptors and understand which processing conditions govern bandgap, defects, and recombination losses. The models will form part of a closed-loop framework in which active learning and Bayesian optimization guide successive synthesis experiments. This creates methodological challenges in learning from small datasets, calibrating predictive uncertainty, incorporating experimental constraints, and balancing scientific understanding with performance optimization. Responsibilities Develop probabilistic models linking synthesis conditions, material properties, and photovoltaic performance. Research active learning and Bayesian optimization methods for selecting informative experiments under practical constraints. Integrate physical and chemical knowledge into models and identify descriptors that help explain defect formation and optoelectronic properties. Develop multi-objective strategies for targeting bandgaps and reducing recombination losses. Work closely with the experimental postdoc and researchers at DTU Electro to design experiments, interpret results, and evaluate the closed-loop framework. Publish scientific papers and present results at leading machine learning conferences and relevant interdisciplinary venues. Contribute to teaching and the supervision of PhD and MSc/BSc students. Qualifications You should have a PhD in machine learning, statistics, computer science, applied mathematics, or a related field. We are looking for candidates with: A strong background in probabilistic machine learning, with experience in areas such as Gaussian processes, Bayesian neural networks, uncertainty quantification, active learning, Bayesian optimization, or experimental design. Proven experience implementing machine learning methods in Python using relevant scientific computing frameworks. A strong publication record in machine learning or closely related fields. Motivation and creative problem-solving skills, with an interest in developing methods that address experimental challenges. Excellent communication and writing skills in English and an enthusiasm for interdisciplinary collaboration. Experience with scientific machine learning, incorporating domain knowledge into models, or learning from experimental data would be an advantage. Prior expertise in materials science or photovoltaics is not required. As a formal qualification, you must hold a PhD degree (or equivalent).

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DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice.

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a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility. Salary and terms of employment The appointment will be based on the collective agreement with the Danish Confederation ofProfessional Associations. In addition, supplements can be negotiated in accordance with DTU’s salary structure for scientific staff: www.inside.dtu.dk/en/human-resources/during-employment/salary/salary-structures. The period of employment is 2 years. The starting date is 1 January 2027 (or according to mutual agreement). The position is a full-time position. You can read more about career paths at DTU here