Hardware & Embedded Systems Lab (HESL)

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Employment Opportunities

This project is funded by National Research Foundation (NRF), Singapore for five years 2016-2020. The project will be undertaken in the Hardware and Embedded Systems Lab (HESL), School of Computer Engineering, Nanyang Technological University, Singapore.

Project Description

The project aims to develop a sensing and management infrastructure for agile transportation. Mass deployment of low-cost smart sensors for localized detection of traffic data/incidents will be used to collaboratively provide high level information of incidents, congestion and traffic patterns by implementing a scalable IoT infrastructure with wireless smart camera networks. In addition, growing coverage of smart mobile devices, data collection through crowd sourcing and mobile sensing will also provide passengers and operators access to great amounts of real-time data. This information will enable users to instantly adapt their itineraries to newly available information, leading to enhanced comfort and travel experience of users. Operators of flexible mobility services could also use this information collected over time to adjust public transport routes or schedules according to the current demand situation. The resulting mutual adaptation could ultimately lead to a symbiotic relationship between demand and supply in which both sides would adjust their behaviour on a similar time scale. This symbiosis will hence lead to a more resilient and efficient self-organizing transport system.

 

A.      Senior Research Fellows and Research Fellows

 

Individuals for this role must have a PhD degree and are expected to be recognized experts in one or a combination of the following research areas. Candidates with relevant post-doctoral experience, strong publication track record, and strong project management skills, will be considered for senior positions.

 

Research Area: Collaborative Sensing for Real-Time Traffic Management


 

Responsibilities:

·    ​Develop state-of-the-art algorithms in one or all of the following areas: object detection/classification, object tracking, object behaviour analysis, crowd density estimation, visual odometry, stereo vision, structure from motion, multi-sensor fusion, deep learning (convolutional neural networks), etc.

·     Develop collaborative vision-based sensing methods for localized detection of traffic data/incidents e.g. congestion on roads, accidents, broken-down vehicles, traffic violation, crowd density estimation, etc.

·         Perform ground truth labelling of traffic videos, test and validate algorithms.

·      Devise algorithms for scalable wireless smart camera networks e.g. optimal camera placement, reliable data communication, etc.

·         Devise multimodal sensing and machine learning techniques for adaptive virtual-right-of-way enforcement.

·         Publish research findings in peer-reviewed journals and conferences.

 

Requirements:

·   Proven expertise and research record in: (i) state-of-the-art computer vision algorithms, (ii) machine learning, (iii) wireless visual sensor network, or (iv) graph algorithms and optimization algorithms.

·         Experience in C, C++, MATLAB coding or similar programming languages

·         Experience in OpenCV, and related computer vision and machine learning libraries.

·         Experience in working on transportation-related projects is preferred.

 

Research Area: Algorithms for Dynamic Route Planning and Demand-Aware Scheduling

 

Responsibilities:

·         Develop algorithms for time-dependent, multi-criteria, multi-modal route planning.

·         Develop algorithms for optimal demand-dependent scheduling of multi-modal transit services.

·         Perform simulation-based validation of the proposed algorithms.

·         Publish research findings in peer-reviewed journals and conferences.

 

Requirements:

·         Proven expertise and research record in graph algorithms and combinatorial optimization.

·         Experience in working on transportation-related projects is preferred.

·         Knowledge of C++ or similar programming language.

 

Research Area: Multimodal Traveller Information System

 

Responsibilities:

·         Develop techniques for pedestrian navigation assistance.

·         Explore indoor location tracking methods for inter-modal transfer guidance.

·         Design a multimodal travel guidance system and oversee its implementation as a mobile application.

·    ​  ​Perform simulation-based analysis of the impact of service disruptions on the public transportation network.

·         Develop and evaluate guidance strategies during service disruptions.

·         Publish research findings in peer-reviewed journals and conferences.

 

Requirements:

·     Knowledge of transportation network modelling, traffic simulation and geographical information systems (GIS).

·         Experience in the design or development of navigation applications will be an advantage.

·         Proven research record in the field of intelligent transportation systems (ITS).

·         Good programming and software development skills.

 

 

B.      Research Associates / Research Assistants

 

We invite applicants with Bachelors or Master’s degree in one or a combination of the following areas.

 

Research Area: Algorithms and Architecture for Low Cost Smart Sensing

 

Responsibilities:

·         Implement computer vision and machine learning algorithms in an embedded development environment

·         Develop distributed and collaborative algorithms for low-cost sensing and analytics

·     Develop design methodologies for heterogeneous computing (microcontrollers, FPGA, GPU) using open source research frameworks (e.g. LLVM, VTR, etc.)

·     Implementing a demonstrable IoT infrastructure with wireless smart camera networks for real-time traffic management

 

Requirements:

·         Familiarity in computer vision and machine learning algorithms

·     Strong in digital hardware design e.g. Altera/Xilinx FPGA, Synopsys, etc. and programmable system-on-chip development

·         Strong embedded or GPU programming skills

·         Proficient in C, C++, MATLAB coding or similar programming languages

·         Familiarity in OpenCV, and related computer vision and machine learning libraries.

·         Familiarity with parallel programming languages e.g. CUDA and Open CL

·     Expertise in application development using camera sensors (monocular, stereo, IR) and wireless sensor networks is preferred

 

Research Area: Data Analytics for Traffic and Travel Demand Estimation

 

Responsibilities:

·         Develop data fusion methods for integrating traffic / travel demand data from multiple sources.

·         Explore machine learning methods for predictive analytics.

·         Develop solutions for inferring travel modes and routes from smartphone location data.

 

Requirements:

·         Good working knowledge of data modelling, statistical methods and data analytics.

·         Experience in scientific programming languages such as MATLAB and R.

 

Application Materials:

1.       Cover letter

2.       Most recent CV

3.       A statement of past experiences and how they are relevant to the position applied

4.       Two referees or/and two letters of recommendation

 

Please email the application materials to siewkei_lam@pmail.ntu.edu.sg.

 

Start Date:

As soon as possible.

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