Course Summary
This NFQ Level 9, 30 ECTS Credits Masters Degree 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 one year part time with 30 credits of taught modules delivered online.
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.
Application Closing Date : 16th August 2024
Entry Requirements
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. 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.atu.ie/recognition-of-prior-learningwhich our dedicated RPL portal or by contacting our admissions team at admissions.sligo@atu.ie .
Programming knowledge (C or Python Programming) and Level 8 Engineering Maths are pre-requisites to the course.
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.
Programme Fees
Academic Year 2024/25 Fees
Total Programme Fee: €3,600
Funded Places Available
Successful applicants to this course may receive funding for their fees on this course through the HCI Pillar 3 Micro-Credentials Learner Fee Subsidy initiative. The number of funded places is limited and will be offered on a first-come, first-served basis.
*Micro-credentials Subsidised Fee: €100
Please see here the eligibility criteria - HCI-Micro-credentials-Fee-Subsidy-Eligibility-Criteria.pdf (hea.ie)
Applicants should note that they can only be registered on one programme at a time (including a micro-credential), at ATU during the academic year (September to May).
To help make the payment of fees more manageable for students who are self-funding their studies, tuition fees can be paid through payment instalment plans at ATU Sligo. For further information on instalment plans, please visit our Fees and Funding webpage.
If you apply and are approved for an online course at ATU Sligo, you will be required to pay a non-refundable deposit of €250 to secure your place. Your deposit will then be credited against the course fees once you are registered as a student. Students at ATU Sligo are also eligible to claim tax relief at the standard rate for tuition fees.
For further information and guidance about Fees and Funding for online and part-time courses at ATU Sligo, click here.
If you are seeking to take your exams online, and you meet the eligibility criteria (overseas students and those with extenuating circumstances), an additional examinations fee will apply. For further information, please visit our Examinations webpage.
Course Format
Semester 1
| Title | Credits |
|---|---|
| Applied Linear Algebra | 05 |
| ADAS and Autonomous System Architecture | 05 |
| Machine Learning | 05 |
Semester 2
| Title | Credits |
|---|---|
| Multiple View Geometry in Computer Vision | 05 |
| Modelling, Simulation and Test Methods for Advanced Driver Assistance Systems | 05 |
| Sensor Fusion | 05 |