Scientist, Offshore & Marine Engineering, TCOMS

Visa Hunt; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Visa Hunt seeks a PhD-qualified researcher to advance autonomous vessel navigation, collision avoidance, and energy-efficient trajectory planning in dynamic maritime environments. You will develop physics-informed, data-driven and control-oriented models and contribute to ROS, MATLAB/Simulink, Python-based simulations.

You will collaborate with multidisciplinary teams, validate algorithms through simulation and field tests, and contribute to project deliverables and publications under funded

Qualifications

• PhD in marine/engineering field as specified.
• Experience in autonomous vessel navigation, collision avoidance, and trajectory planning.
• Proficiency with physics-informed or data-driven methods using sensor data.
• Strong programming in C++, Python, MATLAB/Simulink, or Linux; ROS experience is advantageous.

Responsibilities

• Develop, implement, and validate autonomous ship navigation, collision-avoidance, and energy-efficient trajectory-planning algorithms for dynamic and constrained maritime environments.
• Develop physics-informed, data-driven, and control-oriented models for vessel manoeuvring, propulsion, energy consumption, and ship-performance assessment using simulation and full-scale operational data.
• Contribute to simulation and digital-twin platforms by integrating vessel models, environmental disturbances, onboard sensor data, and real-time control functions using ROS, MATLAB/Simulink, Python, or C++.
• Develop and evaluate situation-awareness functions, including sensor fusion, filtering, localisation, perception, and uncertainty analysis, to support safe autonomous vessel operations.
• Perform closed-loop simulation, software testing, debugging, and experimental validation of navigation and control algorithms, including model-scale or field demonstrations where applicable.
• Collaborate with multidisciplinary research teams, industry partners, and technology providers, and contribute to project deliverables, technical reports, proposals, and publications under funded research programmes.
• Perform other tasks as assigned by the supervisor.

Skills

Autonomous navigation

Trajectory planning

Sensor data integration

Education

PhD in Marine Technology, Naval Architecture, Ocean Engineering, Control Engineering, Robotics, or related field

Tools

ROS

MATLAB/Simulink

C++

Python

Linux

Digital twins

Real-time simulation

Job description

Responsibilities

• Develop, implement, and validate autonomous ship navigation, collision-avoidance, and energy-efficient trajectory-planning algorithms for dynamic and constrained maritime environments.


• Develop physics-informed, data-driven, and control-oriented models for vessel manoeuvring, propulsion, energy consumption, and ship-performance assessment using simulation and full-scale operational data.


• Contribute to simulation and digital-twin platforms by integrating vessel models, environmental disturbances, onboard sensor data, and real-time control functions using ROS, MATLAB/Simulink, Python, or C++.


• Develop and evaluate situation-awareness functions, including sensor fusion, filtering, localisation, perception, and uncertainty analysis, to support safe autonomous vessel operations.


• Perform closed-loop simulation, software testing, debugging, and experimental validation of navigation and control algorithms, including model-scale or field demonstrations where applicable.


• Collaborate with multidisciplinary research teams, industry partners, and technology providers, and contribute to project deliverables, technical reports, proposals, and publications under funded research programmes.


• Perform other tasks as assigned by the supervisor.

Requirements

• PhD in Marine Technology, Naval Architecture, Ocean Engineering, Control Engineering, Robotics, or a related field.


• Experience in autonomous vessel navigation, collision avoidance, motion or trajectory planning, dynamic-system modelling, real-time control, or maritime energy optimisation.


• Experience with physics-informed, data-driven, grey-box, machine-learning, or system-identification methods using operational or sensor data is highly desirable.


• Strong programming skills in C++, Python, MATLAB/Simulink, or Linux, with experience in ROS, embedded systems, digital twins, or real-time simulation considered advantageous.

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.