PhD Student in AI and Digital Twins for Resilient Energy Systems

## Job Title PhD Student in AI and Digital Twins for Resilient Energy Systems ## About Us At RISE, the unit Connected Intelligence conducts applied research and development at the meeting point between artificial intelligence, connected systems and the physical world. We build intelligent, data-driven solutions that turn sensor data, models and real-time information into decisions – for industry, public agencies and society. Our team is interdisciplinary and hands-on. We are a group of researchers who develop practical, trustworthy AI solutions together with industry partners, public agencies and academia. As a PhD student you will be employed at RISE and enrolled as a doctoral student at KTH Royal Institute of Technology, with an academic supervisor at KTH in addition to your supervisors at RISE. ## Responsibilities - Develop AI and machine-learning methods for modelling, monitoring and forecasting in district heating and cooling networks and building energy systems - Build and validate digital twins that mirror the behaviour of these energy systems and their assets in real time - Investigate how data-driven methods can improve the resilience and efficiency of district heating/cooling and building energy systems against faults, disturbances and changing conditions - Combine physics-based models with data-driven approaches (e.g. hybrid and physics-informed machine learning) - Validate methods on real data and in relevant testbed or simulation environments together with energy utilities, property owners and research partners - Publish your results in leading international conferences and journals, and present them in research and industry forums - Contribute to research and innovation projects within the unit ## Requirements - A Master’s degree (or equivalent) in computer science, electrical or energy engineering, applied mathematics, physics or a closely related field - Solid foundation in machine learning and/or modelling and simulation - Good programming skills (e.g. Python) - A strong interest in energy systems – especially district heating/cooling and building energy systems – and in digital twins - Ability to work independently as well as in a team - Excellent communication skills in English, written and spoken ## Nice to Have - Experience with deep learning and modern AI frameworks (e.g. PyTorch, TensorFlow) - Strong knowledge of energy systems, especially district heating/cooling and building energy systems, combined with strong modelling and simulation skills - Experience with digital twins, simulation or physics-informed/hybrid modelling - Experience working with time-series data, sensor data or real-time systems - Experience with optimisation, control or uncertainty quantification - Prior research experience or scientific publications - Good communication skills in Swedish ## Personal Qualities - A strong technical interest and a desire to work at the forefront of technology - Curiosity and a drive to learn, explore and solve complex problems - Strong analytical skills - Communicative and able to collaborate with both technical and non-technical stakeholders - Proactive, with the ability to take initiative and see the bigger picture in complex systems ## Benefits - The opportunity to do impactful research on a strategically important societal challenge - Access to real data, testbeds and simulation environments together with leading partners - Close collaboration with experienced researchers and industry partners - A combination of applied research and academic training, leading to a PhD - A flexible, supportive and research-driven work environment ## About the Role The position is a full-time, time-limited doctoral employment, normally up to five years including approximately 20% departmental work, leading to a PhD. The role is based in Kista, Stockholm, and you are expected to spend 3 days per week in KTH, Campus Valhallavägen for coursework, research collaboration, and possibly teaching duties. Because some projects may be security-sensitive, a security clearance may be required now or in the future.