Research Assistant

Job Description

The College of Information Technology at the United Arab Emirates University is looking for an outstanding Research Assistant to join a research project titled “Towards Smart Cities: Federated Learning Framework for Autonomous Vehicle Motion Planning”. This research aims to develop a learning-based global path-planning system that captures real-world dynamics, create a real-time vehicular communication network simulator for realistic mobility and signaling analysis, and integrate both into a federated learning-based intelligent agent capable of generating improved global paths through shared data across connected vehicles. The research proposes a privacy-preserving and scalable federated learning framework that allows autonomous vehicles to collaboratively improve global path planning by sharing only model updates rather than raw data, overcoming the bandwidth, privacy, and latency limitations of centralized methods through lightweight, real-time–capable architectures such as split learning. The candidate for this position should preferably have a degree in Computer Science/Engineering / Information Technology / Electronics and Communications Engineering or any related field, The ideal candidate should possess strong expertise in machine learning—particularly reinforcement learning, imitation learning, and deep learning—along with solid knowledge of global path-planning methods for autonomous vehicles. They should be proficient in developing and working with real-time simulators, vehicular networks, and edge computing frameworks, as well as skilled in Python, C++, and robotics tools such as ROS or simulation environments like CARLA, SUMO, or ns-3. A strong research background is essential, including experience conducting literature reviews, designing algorithms, and producing high-quality journal publications. The candidate should also be capable of deploying AI models on real-world hardware platforms and excel in analytical thinking, problem-solving, and interdisciplinary collaboration. The candidate will contribute to a multi-phase research program focused on advancing global path planning for autonomous vehicles (AVs), developing real-time vehicular network simulation tools, and designing federated learning frameworks for large-scale AV deployment. The role involves conducting high-impact research, publishing in top-tier journals, and developing novel AI-based systems across three major phases of the project. The candidate will support the development of a next-generation learning-based system designed to continuously improve its global path-planning capabilities using data-driven methods. The candidate will lead the design and implementation of a real-time vehicular network simulator to support experimentation, communication analysis, and AV system testing. The candidate will contribute to the creation of a distributed learning system enabling collaborative AV path planning across multiple agents without compromising data privacy.

Minimum Qualification

B.Sc. degree in Computer Science/Engineering / Information Technology / Electronics and Communications Engineering or any related field

Preferred Qualification

Ph.D. degree in Computer Science/Engineering / Information Technology / Electronics and Communications Engineering or any related field

Close Date Kindly apply before the closing date.

31/03/2026

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