DCU Faculty of Engineering and Computing: Graduate Training Elements

The School of Computing is a stimulating environment for research, particularly in the areas of localization, data analytics, software engineering, scientific computing and cloud computing. It currently has 75 postgraduate research students and a wide range of funded projects at national and international level. 

Selecting Optional Graduate Training Elements (GTEs) 

During their registration, all research students may take a mix of credit-bearing modules (Graduate Training Elements or GTEs), and other non-accredited education opportunities such as workshops, seminars and short courses. These opportunities provide both discipline-specific and transferable skills and knowledge to support students in their research and enhance their research qualification. Engagement in these activities is an important aspect of the graduate researcher experience. 

The Graduate Studies Office organises a broad range of non-accredited workshops and events to support research student skills development. Research students can register for GSO organised workshops and events via the GSO website. Email queries should be directed to training.graduatestudies@dcu.ie.

Students following the structured pathway must attain a minimum of 20 credits in accordance with university structured PhD requirements. Students should take at least one module each from the discipline-specific, and transferable skills lists of modules.

Progression 

The structured pathway for each PhD student should be discussed and agreed in the first instance with the supervisor and progress recorded on the annual PGR2 form. Students should register for their approved GTE modules during the online registration process.

Induction and Training  

Students are encouraged to take advantage of the additional training opportunities offered by the 

Graduate Studies Office (GSO) and by the School as appropriate. All students are required to attend the orientation and induction sessions organised by GSO during year one. GSO communicates details of their training schedule to each student at the beginning of each semester. First-year students are also required to take the Online Research Integrity Training module during year one of their studies.

(7.5 ECTS unless otherwise stated)

• Blockchain: Basics and Applications – CSC1148 (Sem 1)
• Cryptography and Number Theory – CSC1132 (Sem 1)
• Data Analysis and Machine Learning I – EEN1083  (Sem 1)  
• Data Management and Visualisation – CSC1143 (Sem 1)
• Energy Auditing & Energy Management – MEC1058 (Sem 1)  
• Energy System Decarbonisation – EEN1079 (Sem 1)
• Foundations of Artificial Intelligence – CSC1147 (Sem 1)
• Foundations of Natural Language Processing – CSC1122 (Sem 1)
• Foundations of Statistical Analysis & Machine Learning – CSC1181 (Sem 1)
• Human factors in AI – CSC1186 (Sem 1)
• Image Processing & Analysis (Plus) – EEN1044 (Sem 1)
• Intro to DevOps – CSC1037 (Sem 1) (5ECTS)
• Natural Language Technologies – CSC1110 (Sem 1)
• Programming II – CSC1034 (Sem 1) (5ECTS)
• Secure Programming – CSC1135 (Sem 1)
• Solid-State Electronics & Semiconductor Devices – EEN1049 (Sem 1)
• Wireless/Mobile Communications – EEN1043 (Sem 1)
• Advanced Machine Learning – CSC1187 (Sem 2)  
• Advanced Sustainable Energy Systems – MEC1059 (Sem 2)   
• AI, Information and Info Seeking - CSC1138 (Sem 2)    
• Computer Vision – EEN1001 (Sem 2)    
• Data Analysis and Machine Learning – EEN1072 (Sem 2)
• Data Analytics and Data Mining – CSC1144 (Sem 2)    
• Formal Programming – CSC1136 (Sem 2)    
• Global Sustainable Development Challenges –  MEC1068 (Sem 2)  
• Machine Translation – CSC1179 (Sem 2)  
• Mathematical Methods /Computational Science – CSC1139 (Sem 2)
• Network Security – CSC1134 (Sem 2)
• Software Process Quality – CSC1137 (Sem 2)  

(7.5 ECTS unless otherwise stated)

• Engaged Research – HUM1022 10 ECTS (Sem 1)

• Professional Research Practice - CSC1131 (Sem 1)

• Advanced Scientific Communication Skills - CSC1129- 5 ECTS (Sem 2)

• Data Governance CSC1151 - 10 ECTS (Sem 2)

• English for Academic Purposes - ESL1016 5ECTS (Sem 2)

• Entrepreneurship for Engineers - EEN1068 (Sem 2)

• Introduction to Engineering Management EEN1053 (Sem 2)

• Research Ethics - PHE1034 - 5 ECTS (Sem 2)

• Enterprise Experience for Graduate Research Students – EEN1014 - 10 ECTS (Sem 1 or 2)

• Qualitative Research Methods – MNA1126 5 ECTS (Year Long)

• Quantitative Research Methods STA1005 5 ETCS (Year Long)

   

   

   

• Graduate Studies Office Orientation Programme

• Online Research Integrity Training Module (Engineering and Computing stream) (non - accredited)

• Postgraduate Tutor & Demonstrating Programme

• Students are also encouraged to engage with centrally and locally offered workshops and seminars that align with their development needs

Graduate Studies organises a broad range of non-accredited workshops and events to support research student skills development. Research students can register for GSO organised workshops and events via the GSO website.

With 40 years of expertise, state-of-the-art laboratories, and a diverse team supervised by globally-recognised faculty members, the DCU School of Electronic Engineering is firmly embedded in the national and international research network. Much of our research involves collaboration with academic institutions, private companies and public bodies. Our structured PhD programmes enable postgraduate students to complement their research with critical skills like communication, commercialization and entrepreneurship. This document details a suggested structured doctoral pathway for graduate researchers in the School of Electronic Engineering. While the main focus for each research candidate is to complete a piece of original research presented in thesis format, students are also supported in developing a range of skills and competencies through taught modules and other learning opportunities. 

Selection and Registration

During the registration, all research students may take a mix of credit-bearing modules (Graduate Training Elements or GTEs), and other non-accredited education opportunities such as workshops, seminars and short courses. These opportunities provide both discipline-specific and transferable skills and knowledge to support students in their research and enhance their research qualification. Engagement in these activities is an important aspect of the graduate research experience. 

Typical modules taken by Electronic Engineering PhD students are shown in the listing overleaf. 

Students who complete a minimum of 20 GTE credits, in addition to the 270-ECTS thesis, will be recognized as having completed a structured PhD. At least one module should be from the list of discipline-specific modules and one from the list of transferable skills modules. The modules chosen on the structured pathway should be discussed and agreed in the first instance with the supervisor and progress reported on the annual PGR2 form. Students should register for their approved GTE modules during the online registration process.

Progression 

The Structured Pathway work plan for each student should be discussed and agreed in the first instance with the Supervisor and progress (including confirmation of completion of the Online Research Integrity Training Module and other modules) recorded on the annual PGR2 form.   

Induction and Training

Students are encouraged to take advantage of the additional training opportunities offered by the Graduate Studies Office (GSO) and by the School as appropriate. All students are required to attend the orientation and induction sessions organized by GSO during Year One. GSO communicates details of their training schedule to each student at the beginning of each semester. First-year students are also required to take the Online Research Integrity Training module during Year One of their studies.

(7.5 ECTS unless otherwise stated)

• Blockchain: Basics & Applications CSC1148 (Sem 1)
• Data Analysis and Machine Learning 1 EEN1083 (Sem 1)
• Energy Auditing & Energy Management MEC1058 (Sem 1) 
• Energy System Decarbonisation EEN1079 (Sem 1)
• Image Processing & Analysis (Plus) EEN1044 (Sem 1)
• Mathematical Techniques and Problem Solving EEN1054 (Sem 1)
• Natural Language Technologies CSC1110 (Sem 1)
• Network Performance EEN1058 (Sem 1)
• OOP with Embedded Systems EEN1035 (Sem 1)
• Photonic Devices EEN1067 (Sem 1)
• Real-Time Digital Signal Processing (DSP) EEN1073 (Sem 1)

• Solid-State Electronics & Semiconductor Devices EEN1049 (Sem 1)

• Wireless/Mobile Communications EEN1043 (Sem1)
• Advanced Sustainable Energy Systems MEC1059 (Sem 2)
• Computer Vison EEN1001 (Sem 2)
• Connected Embedded Systems EEN1071 (Sem 2)

• Global Sustainable Development Challenges MEC1068 (Sem 2)

• Photonic Applications and Technologies EEN1076 (Sem 2)

• 3D Interface Technologies EEN1057 (Sem 2)

   

• Engaged Research HUM1022 10 ECTS (Year Long)

• Enterprise Experience for Graduate Research Students EEN1014 10 ECTS (Sem 1 & 2)

• Advanced Scientific Communication Skills CSC1129 5 ECTS (Year Long)

• Qualitative Research Methods MNA1126 (Year Long)

• Qualitative Research Methods STA1005 (Year Long)

• English for Academic Purposes - ESL1016 5ECTS (Sem 2)

• Entrepreneurship for Engineers EEN1068 (Sem 2)

• Introduction to Engineering Management EEN1053 (Sem 2)

• Research Ethics PHE1034 5 ECTS (Sem 2)

   

   

• Graduate Studies Office Orientation Programme
• Online Research Integrity Training Module (Engineering and Technology stream) (non - accredited)
• Postgraduate Tutor & Demonstrating Programme
• Students are also encouraged to engage with centrally and locally offered workshops and seminars that align with their development needs

Graduate Studies organises a broad range of non-accredited workshops and events to support research student skills development. Research students can register for GSO organised workshops and events via the GSO website

The School of Mechanical & Manufacturing Engineering has a diverse and rich history of impactful research in the areas of Mechanical & Manufacturing Engineering. The outputs from this are evident in the many top-ranking journal papers, books, patents, and research award emanating from this research. Most significantly many of the research projects involve close ties with industry, multi disciplinarity, international collaboration, and the development of the cutting-edge technologies required for next generation engineering products and services. Specific areas of research strength within the School include Advanced Processing Technologies and Bioengineering. Our structured PhD programmes enable postgraduate students to complete their research with important discipline-specific and generic skills such as communication, commercialisation, and entrepreneurship.

The below details a suggested doctoral pathway for graduate researchers in the School of Mechanical & Manufacturing & Engineering. While the main focus for each research candidate is to complete an original research project, students are also supported in developing a range of skills and competencies through taught modules and other learning opportunities.

Selection and Registration

During registration, all research students may take a mix of credit-bearing modules (Graduate Training Elements, GTEs). Other non-accredited educational opportunities such as seminars, workshops, and short courses are also available. First-year students are required to take the Online Research Integrity Training module during year one of their studies.

The Graduate Studies Office organises a broad range of non-accredited workshops and events to support research student skills development. Research students can register for GSO organised workshops and events via the GSO website https://www.dcu.ie/graduatestudies

These opportunities provide both discipline-specific and transferable skills and knowledge to support students in their research and enhance their research qualification. Engagement in these activities is an important aspect of the graduate research experience. Students should register for their approved GTE modules during the online registration process.

Students who complete a minimum of 20 GTE credits, in addition to the 270-ECTS thesis, will be recognized as having completed a structured PhD. At least one module should be from the list of discipline-specific modules and one from the list of transferable skills modules.

Progression 

The modules chosen on the structured pathway should be discussed and agreed in the first instance with the supervisor and progress reported on the annual PGR2 form.   

Induction and Training 

Research students are also encouraged to take advantage of additional training opportunities offered by the Graduate Studies Office as appropriate throughout their period of study. Year One students are expected to attend orientation sessions, the GSO- and library-run programmes and other relevant induction sessions at the time of initial registration.  

(7.5 ECTS unless otherwise stated)

• Biosafety & Laboratory Standard Operating Procedures BIO1028 5 ECTS (Sem 1,2)
• Data Analysis and Machine Learning I EEN1083 (Sem 1)   
• Energy Auditing and Energy Management MEC1058 (Sem 1)   
• Energy System Decarbonisation EEN1079 (Sem 1)   
• mage Processing and Analysis (Plus) EEN1044 (Sem 1)   
• Manufacturing System Simulation MEC1071 (Sem 1)
• Natural Language Technologies CSC1110 (Sem 1)   
• Solid-State Electronics & Semiconductor Devices EEN1049 (Sem 1)   
• Surface Engineering and Tribology MEC1084 (Sem 1)
• Wireless / Mobile Communications EEN1043 (Sem 1)   
• Advanced FEA MEC1054 (Sem 2)
• Advanced Machine Learning CSC1187 (Sem 2)   
• Advanced Sustainable Energy Systems MEC1059 (Sem 2)   
• Advanced Topics in Machine Learning EEN1015 (Sem 2)
• Computational Thermo-Fluid Dynamics MEC1056 (Sem 2)
• Global Sustainable Development Challenges MEC1068 (Sem 2)   
• LabVIEW, Data Acquisition, Analysis & Control MEC1073 (Sem 2)
• Manufacturing Process Analysis & Tool Design MEC1069 (Sem 2)
• Turbomachinery MEC1061 (Sem 2)
• Whole Life Cycle Analysis MEC1060 (Sem 2)

• Engaged Research HUM1022 10ECTS (Sem 1)  
• Engineering Management and Product Analysis MEC1078 (Sem 1)
• Enterprise Experience for Graduate Researchers EEN1014 (Sem 1 & 2)
• Qualitative Research Methods MNA1126 5ECTS (Sem 1 & 2)
• Quantitative Research Methods STA1005 5ECTS (Sem 1 & 2)
• Research Ethics PHE1034 5ECTS (Sem 1) 
• Research Practice & Methodology MEC1057 (Sem 1)
• Advanced Scientific Communication Skills CSC1129 5ECTS (Sem 2) 

• English for Academic Purposes - ESL1016 5ECTS (Sem 2)

• Entrepreneurship for Engineers EEN1068 (Sem 2)
• Introduction to Engineering Management EEN1053 (Sem 2)  

• Graduate Studies Office Orientation Programme

• Online Research Integrity Training Module (Engineering and Technology stream) (non - accredited)

• Students are also encouraged to engage with centrally- and locally-offered workshops and seminars that align with their development needs

• Postgraduate Tutor & Demonstrating Programme

Graduate Studies organises a broad range of non-accredited workshops and events to support research student skills development. Research students can register for GSO organised workshops and events via the GSO website.

The Centre for Research Training in Machine Learning is designed to address the urgent industry demand for ML talent. The Centre will produce academically outstanding, industry-ready PhD graduates in tightly connected cohorts. These graduates will be future leaders managing the disruption that ML is causing across industry and society, and will strengthen the reputation of Ireland as a global hub for ML education, research, and application.  

The Centre is a collaboration between University College Dublin (UCD), Dublin City University (DCU), and the Technological University of Dublin (TUD). It brings together 57 ML-focused, internationally recognised supervisors who work at the cutting-edge of ML research and its application. Students will benefit from a world-class, inter-institutional programme in a mature interdisciplinary environment that emphasises state-of-the-art research with an industry-relevant and entrepreneurial focus. The activities at the Centre are built around four pillars:

• ML Fundamentals: The fundamental theory, algorithms, techniques, and technologies on which ML is based.
• ML in Society: From the displacement of jobs to the creation of filter bubbles, ML is having an enormously transformative effect on society which needs to be examined, understood, addressed, and communicated.
• ML Practice: As ML technologies have moved out of the lab, a body of best practice has emerged around how to design, develop, deploy, and maintain ML solutions; as well as how to organise the teams that do this work and the projects that they do.
• ML Applications: ML is having a disruptive effect on industries from fashion to agriculture which is driving new ways of operating in these industries and new ML approaches to match industry-specific demands.

Selection and Registration

All modules in this Pathway are core (mandatory) such as ML Bootcamp (10 ECTS UCD); Industry Placement (EEN1014 10 ECTS DCU), Annual Summer School where attendance is compulsory.  Students are also expected to take 30 credits of taught modules from any of the host institutions.  Students should register for their approved GTE modules during the online registration process.

Progression

The Structured Pathway work plan for each student should be discussed and agreed in the first instance with the Supervisor and progress (including confirmation of completion of the Online Research Integrity Training Module and other modules) recorded on the annual PGR2 form.   

Induction and Training 

All students are required to attend the orientation and induction sessions organised by GSO during year one. GSO communicates details of their training schedule to each student at the beginning of each semester. First year students are also required to complete and successfully pass the Online Research Integrity Training Module during year one of their studies.  

Year 1

• ML Bootcamp (10 ECTS) UCD

Taught Modules (Total 30 ECTS) from any of the host institutions such as:

• Data Analysis & Machine Learning EEN1083 (7.5 ECTS) DCU

• Advanced Machine Learning CSC1187 (Sem 2) (7.5 ECTS) DCU

• Multivariate Analysis STAT40740 (5 ECTS) UCD

• Deep Learning SPEC9993 (5 ECTS) TU Dublin

• Statistical Interference & Linear Algebra (5 ECTS) UCD

Year 2

• EEN1014 Enterprise Experience for Graduate Research Students (10 ECTS) (This can also be taken in Year 3)

Year 1

• Online Research Integrity Training Module (non - accredited)

• Annual Summer School Year 1 (non-accredited) UCD (attendance is compulsory)

Year 2

• Annual Summer School Year 2 (non-accredited) UCD 

Year 3

• Annual Summer School Year 3 (non-accredited) UCD 

The SFI Centre for Research Training in Digitally-Enhanced Reality (D-REAL) is an innovative, industry partnered, research training programme that equips PhD students with deep ICT knowledge and skills across Digital Platform Technology, Content and Media Technology and their application in Industry sectors. D-REAL postgraduate students will make research breakthroughs in areas such as multimodal interaction, multimodal digital assistants, multilingual speech processing, real-time multilingual translation and interaction, machine intelligence for video analytics and multimodal personalisation and agency.

Whether via multimodal devices such as smart phones, embedded displays and IoT, or virtual assistants and VR/AR experiences, media technology is revolutionising the way we interact, collaborate and behave. D-REAL PhD students will develop skills for next generation human-centric media technology, including:

• machine intelligence-based sensing and understanding of digital content and information,
• its transformation and personalisation
• its multimodal interaction and delivery via speech, text, video, image and VR/AR, and
• its impactful application in multiple industry and societal settings.

D-REAL is funded by Science Foundation Ireland and by the contributions of industry partners.  TCD is the coordinator for this new Research Centre, and the other University partners include; DCU, NUIG, UCD and TU Dublin. All students will be supervised by an academic in DCU and co supervised by an academic from one of the other University partners. For more information on D–REAL, you can visit the website http://d-real.ie/

Selection and Registration

Students are expected to take the core (mandatory) modules. In addition and in conjunction with their supervisors, they should choose up to 20 credits of optional taught modules. Students should register for their approved GTE modules during the online registration process. 

Progression 

The Structured Pathway work plan for each student should be discussed and agreed in the first instance with the Supervisor and progress (including confirmation of completion of the Online Research Integrity Training Module and other modules) recorded on the annual PGR2 form.

Induction and Training  

All students are required to attend the orientation and induction sessions organised by GSO during year one. GSO communicates details of their training schedule to each student at the beginning of each semester. First year students are also required to complete and successfully pass the Online Research Integrity Training Module during year one of their studies.

Year 1

• Setting Sail (5 ECTS) TCD

• Smaointe* Summer School (5 ECTS)

Year 2

• Enterprise Experience for Graduate Research Students EEN1014 (10 ECTS)

• Smaointe* Summer School (5 ECTS)

Year 3

• Smaointe* Summer School (5 ECTS)

Year 4

• Smaointe* Summer School (5 ECTS)

*The Smaointe Summer School will rotate between the partner Institutions and attendance is compulsory

Students should choose an additional 20 credits from the Pathway Document of their registered school:

School of Computing Pathway Document or School of Mechanical & Manufacturing Engineering Pathway Document or School of Electronic Engineering Pathway Document or School of Applied Language & Intercultural Studies Pathway Document or School of Psychology Pathway Document or School of Communications Pathway Document

All module choices will require approval from your supervisor.

Year 1

• Graduate Studies Office Orientation Programme

• Online Research Integrity Training Module - (non - accredited)

The SFI Centre for Research Training in Artificial Intelligence was established in March 2019 with funding of over €14 million from Science Foundation Ireland and an additional €3.3 million from industry and the academic partners. It is Ireland's national centre for PhD-level training in AI and will train more than 120 PhDs across four cohorts, with an intake of 30 students per annum for the next four years. The Centre brings together five of Ireland's seven universities and a team of almost 60 supervisors across the country. 

This Centre aims to create an internationally connected and globally recognised centre of excellence for the training of postgraduate students and the up-skilling of industry-based staff in key technical topics in artificial intelligence. The initiative will provide training in areas related to ethics in artificial intelligence and data analytics, as well as challenges in fairness and transparency of advanced data-driven applications. The proposed Centre for Research Training (CRT) brings together supervisors working across the full spectrum of AI techniques from knowledge representation and reasoning, machine learning, data mining, computer vision, natural language processing, optimisation and decision-making, robotics, and autonomy. The Centre will focus strongly on the development of AI solutions in domains such as smart buildings, mobility and transportation, autonomous vehicles, public service delivery, manufacturing, enterprise, cybersecurity, climate change and environment, agriculture, marine, food production, and natural resources. 

This Centre is a joint initiative between University College Cork, Dublin City University, National University of Ireland Galway, Trinity College Dublin, and the University of Limerick. We offer fully-funded PhD scholarships inclusive of fees, a monthly stipend, and a budget for travel and training. The Centre will produce 120 PhD graduates in the field of artificial intelligence in a world-class cohort-based model, working closely with industry and international academic partners. Every student taking part in the programme will spend a number of internships with industry partners or at international partner laboratories.  

Selection and Registration

Students are expected to take the following Core module; CA639 Artificial Intelligence Methods (10 ECTS). In Year 2, students will take the Industry Placement Module, EE611/A Enterprise Experience for Graduate Research Students (10 ECTS). PhD students will then take an additional 10 credits which have been deemed suitable and in line with their professional development plan and should be agreed with their Supervisor. Students should register for their approved GTE modules during the online registration process. 

Progression

The Structured Pathway work plan for each student should be discussed and agreed in the first instance with the Supervisor and progress (including confirmation of completion of the Online Research Integrity Training Module and other modules) recorded on the annual PGR2 form.   

Induction and Training  

All students are required to attend the orientation and induction sessions organised by GSO during year one. GSO communicates details of their training schedule to each student at the beginning of each semester. First year students are also required to complete and successfully pass the Online Research Integrity Training Module during year one of their studies.

Year 1

Core Modules (Mandatory) – 10 Credits

• Artificial Intelligence Methods – CSC1130 (10 ECTS) 

Year 2

• Enterprise Experience for Graduate Research Students – EEN1014 (Sem 1&2) (10 ECTS) 

Optional Modules – 10 Credits

• Students should choose an additional 10 credits from the Pathway Document of either the School of Computing Pathway Document or School of Mechanical & Manufacturing Engineering Pathway Document or School of Electronic Engineering Pathway Document

• All module choices will require approval from your supervisor

Year 1

• Graduate Studies Office Orientation Programme

• Online Research Integrity Training Module - (non - accredited)

The Centre for Doctoral Training in Advanced Metallic Systems (AMSCDT) is a joint venture between Dublin City University, University College Dublin, University of Sheffield, and University of Manchester. AMSCDT provides high quality training to the next generation of globally competitive doctoral level graduates with the knowledge, skillset, and mindset to lead the future Ireland advanced manufacturing industry. All of our students have an industrial sponsor. It is an opportunity for the students to drive a research project tailored to real world technical challenges, with access to world-class research facilities and expertise. The AMSCDT PhD programme is different to a standard doctoral programme, combining taught technical units with an industrially prescribed doctoral project and professional skills training.

The first part of the training programme is designed to support the conversion of graduates from STEM into materials science and metallurgy, developing core materials knowledge in simulation and modelling techniques and advanced manufacturing technologies, in the first 9 months. 

Our comprehensive Personal and Professional Skills training programme over the 4-year programme accelerates students’ research, leadership, and employability skills, and is led by the University of Sheffield. At 48 ECTS the training programme includes scientific writing and presentations, project management, Equality Diversity and Inclusion, Responsible Research and Innovation, Outreach/Media, Standards, Codes and Specifications, interpersonal and networking skills. By delivering training to the cohort our students have a wider awareness of the metals sector, evidenced by our alumni destinations, with >96% of our graduates securing a senior role in metallurgy in industry or academia. 

Selection and Registration
Dublin City University Students following the structured pathway must attain a minimum of 20 credits in accordance with the university structured PhD Requirements. Students should complete two modules of the four modules offered in Year One, of Semester One. The remaining 5 credits will be attained through a compulsory COMP47670 Data Analytics (5 ECTS) module delivered and assessed by UCD. Students should register for their approved GTE modules during the online registration process.
 

Progression
The Structured Pathway work plan for each student should be discussed and agreed in the first instance with the Supervisor and progress (including confirmation of completion of the Online Research Integrity Training Module and other modules) recorded on the annual PGR2 form.
 

Induction and Training
First-year students are also required to take the Online Research Integrity Training module during year one of their studies and are expected to attend orientation sessions, the GSO - and library- run programmes and other relevant induction sessions at the time of initial registration.

Research students are also encouraged to take advantage of additional training opportunities offered by the Graduate Studies Office as appropriate throughout their period of study.

(30 ECTS)
YEAR 1 Semester 1
(all units compulsory)
University of Sheffield
MAT61005 Phase
Transformations & Solidification
(4 ECTS)
MAT61002 Structure &
Mechanical Properties (4 ECTS)
MAT61001 Advanced Modelling
Techniques (2 ECTS)
University College Dublin
COMP47670 Data Analytics (5
ECTS) Online
YEAR 1 Semester 2
(Choose 2 of 5)
Dublin City University

MEC1056 Computational Thermo-
Fluid (7.5 ECTS)

MEC1073 Labview Data Acquisition,
Analysis and Control (10 ECTS)
MEC1069 Manufacturing Process
Analysis and Tool Design (7.5
ECTS)
 

(48 ECTS)
(all units compulsory)
University of Sheffield

Year 1

MAT6299 Mini Research Project (12 ECTS)
MAT6294 Transformative Technologies (4 ECTS)
MAT61004 Modern Research Environment (4 ECTS)
AER4447 Industrial Training Programme (8 ECTS)
 

Year 2
MAT6297 Public Engagement Project (4 ECTS)
FCE6009 Skills in Action (4 ECTS)
 

Year 3
MAT6011 SME Consultancy Project (4 ECTS)
MAT6291 Standards, Codes & Specifications (2 ECTS)

Year 4
MAT6398 Science and Engineering in the Media (2 ECTS)
 

Years 2, 3 & 4
FCE608 Doctoral Communication Skills (4 ECTS)

• Graduate Studies Office Orientation Programme

• First-year students are also required to take the Online
  Research Integrity Training module during year one of their studies.