Doctoral student in Explainable Machine Learning for Neuromorphic Computing

Örebro University and the School of Science and Technology are looking for a doctoral student for the doctoral programme in Computer Science, concluding with a doctoral degree. Start date: Fall 2026. Project description The focus of the doctoral project is explainable machine learning for neuromorphic computing for predictive materials discovery. The doctoral student will be part of a larger project Brain-inspired AI Design of Topological Magnets for Sustainable Computing. The five-year project is a WASP-WISE-funded collaboration with research groups at Uppsala University and KTH for designing sustainable magnetic materials. The doctoral student will also be part of the Wallenberg AI, Autonomous Systems and Software Programme (WASP) and graduate school. WASP is Sweden’s largest individual research programme ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The programme addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society and industry. Read more: https://wasp-sweden.org/ The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious programme with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between doctoral students, researchers, and industry. Read more: https://wasp-sweden.org/graduate-school/ The programme and the doctoral studentship The doctoral programme consists of courses and an independent resea