Engineering - Connected & Autonomous Vehicles - Sligo
This programme, which has been has been developed in collaboration with industry, specialises in the design and development of Advanced Driver Assistance Systems - the underlying technology of smart and autonomous vehicles. It brings together interdisciplinary concepts such as computer vision, artificial intelligence, vehicle dynamics and advanced sensor systems to provide current engineers with the skills required to design the next generation of automotive technology.
| Award Name | Degree - Masters (Level 9 NFQ) |
|---|---|
| NFQ Classification | Major |
| Awarding Body | Atlantic Technological University |
| NFQ Level | Level 9 NFQ |
| Award Name | NFQ Classification | Awarding Body | NFQ Level |
|---|---|---|---|
| Degree - Masters (Level 9 NFQ) | Major | Atlantic Technological University | Level 9 NFQ |
Duration
2.5 years part-time, online.
Entry Requirements
Applicants with a Level 8 Honours Degree 2.1 or above in Electronic Engineering, Mechatronic Engineering, Mechanical Engineering, Computer Science or a related discipline are eligible to apply for this programme.
Careers / Further progression
Career Opportunities
Students will find employment in Senior Design Positions in Electronic, Mechanical, Mechatronics and Embedded Systems engineering for highly regulated industries. Although primarily directed at the automotive sector, many of the skills such as Machine Learning, Pattern Detection and Computer Vision are highly sought after for R&D roles in other industries such as the medical, agricultural and high-volume manufacturing industries.
Further information
Total Fees: €11,000
Study Hours
It is recommended that students should try to allow for 5-6 hours per week per five-credit module to their studies.
Examination & Assessment
A mix of continuous assessment, laboratory exercise and exams.
Who should apply?
This programme is suitable for those looking to up-skill or re-skill into the Connected and Autonomous Vehicle profession.
Modules
Applied Linear Algebra
ADAS and Autonomous System
Architecture
Environment Detection
Multiple View Geometry in
Computer Vision
Automotive System Safety &
Cybersecurity
Applied Statistics and
Probability
Research Methods
Machine Learning
Vehicle Dynamics and Control
Modelling, Simulation and Test
Methods for Advanced Driver
Assistance Systems
Connected Vehicles
Sensor Fusion
MEng CAV Research
Dissertation
Examination & Assessment
A mix of continuous assessment, laboratory exercise and exams.
Course Coordinator: Shane Gilroy