A fully-funded PhD position is available at the University of Cambridge for research in AI-accelerated discovery of energy materials, with a focus on grid-scale energy storage. The project is interdisciplinary, combining advanced machine learning with experimental datasets at the Cavendish Laboratory. The student will develop computational approaches to understand battery degradation, efficiency, and lifespan. Supervision is provided by Akshay Rao (Physics), James Fergusson (Physics), and Miles Cranmer (DAMTP), with collaboration from the Chemistry department and the spinout company illumion. The position is ideal for candidates with strong backgrounds in physics, chemistry, materials science, applied mathematics, or related fields. The studentship is fully funded for four years, starting October 2025, and covers tuition and stipend. The application deadline is May 10, 2025. Interested candidates should review the flyer for full details and submit their application to the University of Cambridge. The opportunity is part of the broader AI-for-Science community in Cambridge, offering a collaborative and innovative research environment.

A fully-funded PhD position is available at the University of Cambridge for research in AI-accelerated discovery of energy materials, focusing on grid-scale energy storage. The project is interdisciplinary, combining advanced machine learning with experimental datasets at the Cavendish Laboratory. The student will develop computational approaches to understand battery degradation, efficiency, and lifespan. Supervision is provided by Akshay Rao (Physics), James Fergusson, and Miles Cranmer (DAMTP), with collaboration from the Chemistry department and the spinout company illumion. The position is ideal for candidates with strong backgrounds in physics, chemistry, materials science, or applied mathematics. The studentship covers tuition and stipend for four years, starting October 2025. The application deadline is May 10, 2025. Interested applicants should review the flyer for details and apply through the University of Cambridge. The opportunity is part of the broader AI-for-Science community in Cambridge.

A fully-funded PhD position is available at the University of Cambridge for research in AI-accelerated discovery of energy materials, focusing on grid-scale energy storage. The project is interdisciplinary, combining advanced machine learning with experimental datasets at the Cavendish Laboratory. The student will develop computational approaches to understand battery degradation, efficiency, and lifespan. Supervision is provided by Akshay Rao (Physics), James Fergusson, and Miles Cranmer (DAMTP), with collaboration from Chemistry and the spinout company illumion. The position is ideal for candidates with strong backgrounds in physics, chemistry, materials science, applied mathematics, or related fields. The studentship covers tuition and stipend for four years, starting October 2025. The application deadline is May 10, 2025. Interested applicants should review the flyer for details and apply through the University of Cambridge. The opportunity is part of a vibrant AI-for-Science community in Cambridge.

A fully-funded PhD position is available at the University of Cambridge, focusing on AI-accelerated discovery of energy materials for grid-scale energy storage. The project is interdisciplinary, combining advanced machine learning with experimental datasets at the Cavendish Laboratory. The student will develop computational approaches to understand battery degradation, efficiency, and lifespan. Supervision is provided by Akshay Rao (Physics), James Fergusson, and Miles Cranmer (DAMTP), with collaboration from the Chemistry department and the spinout company illumion. The opportunity is ideal for candidates with strong backgrounds in physics, chemistry, materials science, applied mathematics, or related fields. The studentship lasts four years, is fully funded, and starts in October 2025. The application deadline is May 10, 2025. Interested applicants should review the flyer for details and submit their application to the University of Cambridge. The project is part of the broader AI-for-Science community in Cambridge, offering a collaborative and innovative research environment.

A fully-funded PhD position is available at the University of Cambridge, focusing on AI-accelerated discovery of energy materials for grid-scale energy storage. The project is interdisciplinary, combining advanced machine learning with experimental datasets at the Cavendish Laboratory. The student will develop computational approaches to understand battery degradation, efficiency, and lifespan. Supervision is provided by Akshay Rao (Physics), James Fergusson, and Miles Cranmer (DAMTP), with collaboration from Chemistry and the spinout company illumion. The opportunity is ideal for candidates with strong backgrounds in physics, chemistry, materials science, applied mathematics, or related fields. The studentship is fully funded for four years, starting October 2025, and covers tuition and stipend. The application deadline is May 10, 2025. Interested applicants should refer to the attached flyer for details and apply as instructed.

A fully-funded PhD position is available at the University of Cambridge, focusing on AI-accelerated discovery of energy materials for grid-scale energy storage. The project is interdisciplinary, combining advanced machine learning with experimental datasets at the Cavendish Laboratory. The student will develop computational approaches to understand battery degradation, efficiency, and lifespan. Supervision is provided by Akshay Rao (Physics), James Fergusson, and Miles Cranmer (DAMTP), with collaboration from the Chemistry department and the spinout company illumion, as well as the broader AI-for-Science community in Cambridge. Applicants should have strong backgrounds in physics, chemistry, materials science, applied mathematics, or related fields. The studentship is fully funded for four years, starting October 2025, and covers tuition and stipend. The application deadline is 10 May 2025. Interested candidates should review the attached flyer for full details and submit their application to the University of Cambridge. The opportunity is ideal for students passionate about interdisciplinary research at the intersection of AI, materials science, and energy technology.