A fully funded PhD studentship is available at Birmingham City University, UK, focusing on Smart Urban Energy Systems. The research will integrate Deep Learning and Digital Twins to develop Positive Energy Districts, aiming to transform cities into smart and sustainable energy hubs. The project combines advanced AI, real-time energy data, and urban analytics to address real-world sustainability challenges. The studentship offers a £20,780 tax-free annual stipend and full tuition coverage for 36 months. Applicants should have a Master’s degree in Computer Science, IT, AI, Data Science, or a related field, with strong skills in Deep Learning (CNNs, GNNs, DRL), Python programming, and experience or interest in Digital Twins, IoT, and real-time system simulation. Additional desirable qualifications include hands-on experience with Digital Twin platforms, simulation tools, urban energy modelling, and a record of published or presented work in AI, smart cities, or energy systems. The supervisory team includes Dr. Syed Attique Shah (main), Dr Vahid Javidroozi, and Muhammad Ajmal Azad. The application deadline is 17th September 2025, with a start date of 2nd February 2026. Applications must be submitted via the official link, and enquiries can be directed to [email protected]. This opportunity is ideal for candidates passionate about applying AI and digital innovation to urban sustainability.

A fully funded PhD studentship is available at Birmingham City University, UK, focusing on Smart Urban Energy Systems. The research will integrate Deep Learning, Digital Twins, and real-time energy data to transform cities into positive energy hubs. The project aims to advance the use of AI and digital innovation for sustainable urban development, with a particular emphasis on energy optimisation and predictive analytics. The successful candidate will join a team led by Dr. Syed Attique Shah (main supervisor), Dr. Vahid Javidroozi, and Muhammad Ajmal Azad (co-supervisors). Applicants should have a Master’s degree in Computer Science, IT, AI, Data Science, or a related field, with strong skills in Deep Learning (CNNs, GNNs, DRL), Python programming, and experience or interest in Digital Twins, IoT, and real-time system simulation. Additional desirable qualities include hands-on experience with simulation tools, urban energy modelling, and a record of publication or presentation in AI, smart cities, or energy systems. The studentship provides a tax-free stipend of £20,780 per year and covers full tuition fees for 36 months. The application deadline is 17th September 2025, and the start date is 2nd February 2026. Applications must be submitted via the official link; email applications will not be considered. For further enquiries, contact Dr. Syed Attique Shah at [email protected].

A fully funded PhD studentship is available at Birmingham City University, UK, focusing on Smart Urban Energy Systems. The research will integrate Deep Learning, Digital Twins, and real-time energy data to transform cities into positive energy hubs. The project aims to advance the use of AI and digital innovation for sustainable urban development, with a particular emphasis on energy optimisation and analytics. The successful candidate will join a team led by Dr. Syed Attique Shah (main supervisor), Dr. Vahid Javidroozi, and Muhammad Ajmal Azad. Applicants should have a Master’s degree in Computer Science, IT, AI, Data Science, or a related field, with strong skills in Deep Learning (CNNs, GNNs, DRL), Python programming, and experience or interest in Digital Twins, IoT, and real-time system simulation. Additional desirable qualities include hands-on experience with Digital Twin platforms, urban energy modelling, and a record of publications or presentations in AI, smart cities, or energy systems. The studentship provides a tax-free stipend of £20,780 per year and covers full tuition fees for 36 months. The application deadline is 17th September 2025, and the start date is 2nd February 2026. Applications must be submitted via the official link; email applications will not be considered. For enquiries, contact [email protected]. This opportunity is ideal for candidates passionate about applying AI to real-world sustainability and urban energy challenges.