Sensors for Autonomous Vehicles - Sligo

This NFQ Level 9, 30 ECTS Credits Postgraduate Certificate has been developed in collaboration with industry and is aimed at Electronic, Computer, Mechanical and Mechatronic Engineers who wish to develop the skills required to design the next generation of technology for smart and autonomous vehicles.



The programme will run over one year part time with 30 credits of taught modules primarily delivered online with some on-campus workshops.



Key Course Information

Study hours: Whether you are studying part-time online, blended or full-time online, it is very important that you allocate enough study time to your online course to stay focused, reduce stress and achieve your goals. For part-time online or blended learning, it is recommended that you should try to allow for 7 hours per week for a 5 credit module.



Live Lectures: Live lectures normally take place between 6pm and 10pm, Monday to Thursday but this may vary depending on the availability of specific lecturers. . If the Live Classroom scheduled times for the live online lectures do not suit you, recordings will be made available through Moodle.

Semester 1

Title Credits

Applied Linear Algebra 05

ADAS and Autonomous System Architecture 05

Environment Detection 05



Semester 2

Title Credits

Multiple View Geometry in Computer Vision 05

Automotive System Safety & Cybersecurity 05

Applied Statistics and Probability 05

Graduates 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. Programming knowledge (Ideally C++) and Level 8 Engineering Maths are pre-requisites to the course.Applicants who do not meet these criteria but have the willingness to address them will be considered. Candidate interviews and entrance exams will be used to assess suitability for the programme. Graduates who have not obtained this minimum may incorporate other equivalent qualifications and relevant work experience and apply for assessment via the Recognition of Prior Learning (RPL) process. RPL is a process that may allow you to gain admission to a programme or to receive exemptions/ credit for some parts of the programme based on demonstrated learning that you may have achieved through another programme of study or through your work or career. Further information is available at www.myexperience.ie which our dedicated RPL portal or by contacting our admissions team at admissions@itsligo.ie .



In addition, international students, whose first language is not English, will be required to prove their English competency through previous examination results, recognized English language tests such as IELTS (6.5 or equivalent required) and through oral communication skills at interview.

1 year part-time, online.

Career Opportunities

Upon completion students will be eligible to pursue a Postgraduate Diploma or Master of Engineering in Connected and Autonomous Vehicles.



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.