Deskripsi Pekerjaan
Nanyang Technological University (NTU) is seeking a highly motivated and skilled Research Fellow to join our prestigious Transportation Engineering division. This role sits at the intersection of AI-driven analytics and urban infrastructure, tasked with revolutionizing road safety through advanced data modeling.
As a Research Fellow, you will leverage large-scale traffic datasets and cutting-edge artificial intelligence to identify latent road safety risks. Your findings will be instrumental in designing proactive, data-driven interventions that enhance urban mobility and save lives. You will work alongside world-class faculty and have access to state-of-the-art computational resources, contributing to high-impact research publications and real-world policy implementations.
This position is ideal for an ambitious researcher eager to apply machine learning algorithms to complex transportation problems within a globally recognized academic environment. Join us in shaping the future of smart cities and intelligent transportation systems in Singapore.
Tanggung Jawab
- Develop and deploy advanced AI and machine learning models to analyze complex transportation and traffic datasets.
- Identify critical road safety risk factors through predictive modeling and spatial-temporal analysis.
- Design innovative, data-backed engineering interventions to mitigate identified traffic hazards.
- Collaborate with multidisciplinary teams, including urban planners, data scientists, and public policy stakeholders.
- Author high-quality academic manuscripts for peer-reviewed journals and present findings at international conferences.
- Oversee the collection, cleaning, and validation of large-scale traffic sensor and vehicle trajectory data.
- Mentor junior researchers and graduate students on technical methodologies and research best practices.
Kualifikasi
- PhD in Transportation Engineering, Civil Engineering, Data Science, Computer Science, or a closely related field.
- Proven expertise in machine learning, deep learning, or statistical modeling applied to transportation networks.
- Strong programming proficiency in Python, R, or MATLAB.
- Demonstrated experience in handling large datasets (Big Data) and spatial analysis (GIS).
- Solid track record of research publications in reputable transportation or engineering journals.
- Excellent analytical, problem-solving, and critical thinking skills.
- Strong verbal and written communication skills to effectively translate technical insights for stakeholders.