Postdoctoral Fellow - Emirates Cntr. for Mobility Reserch

United Arab Emirates University (UAEU) invites applications for a highly motivated Postdoctoral Fellow to join the Emirates Center for Mobility Research (ECMR) and contribute to the project “Smart Climate-Adaptive Mobility Digital Twin for Sustainable, Safe, and Inclusive Urban Mobility.” The project will develop and validate a real-time AI-enabled digital twin that integrates traffic, public-transport, parking, incident, weather, environmental, accessibility, pedestrian-exposure, and electric-vehicle charging data within one urban mobility intelligence framework. The successful candidate will lead advanced research in multimodal data fusion, dynamic graph modelling, climate-aware spatio-temporal learning, federated intelligence, uncertainty quantification, explainable AI, and multi-objective optimisation. The platform will forecast mobility and climate-related risks, support simulation of adaptive interventions, and deliver a validated prototype, analytics dashboard, scholarly outputs, and potential intellectual property. Grade: 19.

•⁠ ⁠A PhD in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, Electrical Engineering, Software Engineering, Civil or Transportation Engineering, or a closely related quantitative discipline from an accredited university. •⁠ ⁠Demonstrated research experience in machine learning or deep learning, including spatio-temporal modelling, graph learning, sequence modelling, multimodal learning, time-series forecasting, or intelligent decision systems. •⁠ ⁠Strong Python programming skills and hands-on experience with deep-learning frameworks such as PyTorch or TensorFlow. •⁠ ⁠Experience analysing and managing large, heterogeneous, time-series, spatial, geospatial, sensor, or mobility datasets. •⁠ ⁠A strong record of peer-reviewed research publications and the ability to prepare high-quality scientific manuscripts and technical reports. •⁠ ⁠Excellent written and spoken English, strong analytical and organisational skills, and the ability to work independently and within a multidisciplinary team while meeting project milestones. Preferred qualifications: •⁠ ⁠Experience in digital twins, intelligent transportation systems, smart-city mobility, traffic safety, transportation analytics, or connected and autonomous mobility. •⁠ ⁠Experience with graph neural networks, temporal attention or transformer models, multi-task learning, federated learning, explainable AI, privacy-preserving analytics, or uncertainty-aware prediction. •⁠ ⁠Knowledge of multi-objective optimisation, reinforcement learning, control, simulation-based decision support, or operations research. •⁠ ⁠Experience with IoT or sensor integration, edge/cloud computing, real-time data pipelines, dashboards, or deployment-oriented AI systems. •⁠ ⁠Familiarity with GIS, climate or weather data, environmental monitoring, EV-charging systems, accessibility analysis, or hot-climate urban operations. •⁠ ⁠Evidence of interdisciplinary or international collaboration, funded-project experience, stakeholder engagement, patents, software prototypes, or research commercialisation.

Lead advanced research in multimodal data fusion, dynamic graph modelling, climate-aware spatio-temporal learning, federated intelligence, uncertainty quantification, explainable AI, and multi-objective optimisation. Forecast mobility and climate-related risks, support simulation of adaptive interventions, and deliver a validated prototype, analytics dashboard, scholarly outputs, and potential intellectual property.