PhD Digitalisation Engineering

20221007_Automation-Robotics_TUS-Athlone-Campus_002

Introduction

The PhD in Digitalisation Engineering will train and produce highly qualified professionals at the forefront of the specific scientific domains in the technological field of the digital transformation of companies and institutions, namely by studying, developing and implementing innovative solutions with digital technologies.

The programme, based on the creation of knowledge through international cooperation integrated within the European University RUN-EU, aims to provide appropriate training for the exercise of technical and scientific activities, integrating knowledge and developing innovation in multidisciplinary professional environments, with high levels of ethics, environmental awareness, and high standards of demand and competitiveness. This approach enables capable professionals of contributing to economic and industrial development in different regions of the European area.

Offered by Technological University of the Shannon, the School of Technology and Management of the Polytechnic of Leiria (Portugal) , in association with the Polytechnic Institute of Cávado and Ave (Portugal). The PhD in Digitalisation Engineering is part of the educational project of the three institutions, aiming to contribute to common objectives, namely the development and consolidation of centres of competitiveness and innovation in digital technologies, aligning its programme with the areas of smart specialisation of the regions where the institutions are located.

Programme Coordinator

Dr Siobhán Moane – Siobhan.Moane@tus.ie

Faculty

Engineering & Technology

Location

Blended (Limerick & Portugal)

Language

English

Type

Doctoral Degree (PhD)

Length

3 years

Programme Objectives

Students are expected to develop the following knowledge, skills and competences:

Mastery of state-of-the-art digital technologies applied to the automation of processes and services;
Ability to analyse and investigate complex challenges, and propose new solutions incorporating knowledge of digital technologies;
Ability to conceive, design and develop innovative products and/or processes for industrial and service digitalisation;
Ability to lead and collaborate in interdisciplinary and international projects, as well as organize, synthesise, communicate and disseminate the generated scientific knowledge, respecting the ethics and methods of scientific research.

Outline Y1

Research Methodologies – Semester 1 – 5 ECTS

Knowledge and Knowledge development: defining knowledge, types of knowledge and knowledge cycle
Underlying assumptions of each paradigm: Philosophy, Ontology Epistemology and Methodology
Inter-relationships between paradigms: theory generation, and hypothesis testing
Literature review methodology
Critical thinking and scientific thinking
Methodology and methods: experimental, quasi-experimental and non-experimental design, qualitative methods, and mixed methods
Development of project and research proposals

Research Integrity – Semester 1 – 5 ECTS

Ethical responsibilities in the research process.
Ethical challenges during research projects.
Strategies for handling pressure and difficult situations.
Local and professional policies and guidelines regarding research integrity.
Recognising improper conduct in research and procedures to follow in cases of misconduct in accordance with best practices.

Elective 1 – Semester 1 – 5 ECTS

Students must choose one of the following curricular units. For more information, please lock at “Elective I and Elective II – available curricular units”

Data Analytics and Machine Learning
Antennas, Propagation and Remote Sensing
Database and Data Visualisation
Cybersecurity
Intelligent Control
Digitalisation of Production Systems
Intelligent Data Fusion
Processing, Analysis and Coding of Digital Information
Robotics
Smart Cyberphysical and IoT Systems
Energy Transformation
Complementary Skills in Digitalisation Engineering I

Elective 2 – Semester 1 – 5 ECTS

Students must choose one of the following curricular units. For more information, please loock at “Elective I and Elective II – available curricular units”

Data Analytics and Machine Learning
Antennas, Propagation and Remote Sensing
Database and Data Visualisation
Cybersecurity
Intelligent Control
Digitalisation of Production Systems
Intelligent Data Fusion
Processing, Analysis and Coding of Digital Information
Robotics
Smart Cyberphysical and IoT Systems
Energy Transformation
Complementary Skills in Digitalisation Engineering II

Seminar – Semester 2 – 5 ECTS

Writing of a scientific article or a detailed scientific work plan.
Submission of a scientific article to a conference.
Preparation, public presentation and discussion of the scientific work.

Thesis Planning – Annual – 35 ECTS

The contents will depend on the topic chosen by the student, but the study will consist of aspects considered relevant to the topic of research in which the student prepares his thesis plan, including the identification of problems, particularly in an industrial context, the prior evaluation of existing solutions, a survey of the state of the art and the work plan of to be carried out during the PhD. Students will also acquire knowledge about the scientific method and will develop different technological research work approaches.D

Outline Y2

Data Analytics & Machine Learning – Semester – 5 ECTS

Concepts of Data Analytics and Machine Learning
- Data analysis methodologies;
- Collection, generation and deployment of data;
- Data exploration;
- Data pre-processing;
- Machine learning techniques;
- Model evaluation and selection.
Time series analysis
Process Mining
- Business process management;
- Data pre-processing;
- Discovery, compliance and improvement of business processes.
Big Data techniques
- Large-scale data storage and processing
Advanced Data Analytics and Machine Learning techniques
- Reinforcement Learning;
- Deep Learning;
- Generative AI;
- Explainable AI;
- Fair AI;
- Edge AI;
- Training deep neural networks using deterministic methods.
Ethical and privacy issues related to Data Analytics and Machine Learning

Antennas, Propagation & Remote Sensing – Semester – 5 ECTS

Fundamentals of Electromagnetic Theory
- Covers Maxwell’s equations;
- Wave propagation;
- Antenna theory;
- Physical interpretations of electromagnetic phenomena.
Numerical Methods
- FDTD, FEM, MoM;
- High-frequency methods;
- Simulation software tools.
Antenna Modelling
- Antenna fundamentals;
- Types, arrays, and design optimization.
Electromagnetic
- Wave propagation;
- Scattering, and communication/radar applications.
Remote Sensing
- Principles of SAR;
- Microwave remote sensing;
- Passive radar;
- Various applications.
Measurement Techniques
- Near-field/far-field measurements;
- Antenna characterization;
- Scattering parameters;
- Field measurements;
- Radar systems.
Special Topics
- Metamaterials;
- Multi-physics simulations;
- Inverse problems.

Database & Data Visualisation – Semester – 5 ECTS

Distributed DBMS Architecture
Distributed Database Design
Query Processing and Decomposition
Distributed query optimisation
Transaction Management
Distributed DBMS Reliability
Parallel Database Systems
Distributed object Database Management Systems
Object-Oriented Data Model
Data visualisation
Tools for data visualisation

Cybersecurity – Semester – 5 ECTS

Information Security and Cybersecurity
Network Security
Digital Forensic Analysis
Malware Analysis
Information Systems Risk Analysis
Ethics, Compliance, and Human Factor
Practical Cases

Intelligent Control – Semester – 5 ECTS

Introduction to Intelligent Control Systems
- Overview of Control Systems;
- Introduction to Intelligent Control;
- Historical Perspective and Applications;
- Challenges and Opportunities in Intelligent Control.
Machine Learning for Control
- Regression and Classification for Control;
- Reinforcement Learning and Control;
- Deep Learning for Control Applications;
- Case Studies: Control Using Machine Learning.
Evolutionary Algorithms for Control
- Genetic Algorithms for Optimization;
- Genetic Programming for Control;
- Evolutionary Strategies for Control;
- Case Studies: Evolutionary Control Techniques.
Neural Networks for Control
- Introduction to Artificial Neural Networks (ANNs);
- Multilayer Perceptron’s (MLPs) for Control;
- Recurrent Neural Networks (RNNs) for Control;
- Case Studies: Neural Network-Based Control Systems.
Real-world Applications and Case Studies
- Real-world Applications of Intelligent Control.

Digitalisation of Production Systems – Semester – 5 ECTS

Intelligent Production Systems
- Concept;
- Requirements;
- Relationship with Industry 4.0.
Fundamentals of Manufacturing Execution Systems
Introduction to the digital thread and technologies for Industry 4.0
- Approach to digital manufacturing (3D Design, finite element analysis and generative design);
- Additive manufacturing;
- Simulation in production and logistics;
- Digital instructions, virtual and augmented reality;
- Digital Twin;
- Smart machining;
- Cyber-Physical Production Systems.
Organizational concepts for Industry 4.0
- Work 4.0;
- Operator 4.0;
- Impact of Industry 4.0.

Intelligent Data Fusion – Semester – 5 ECTS

Introduction to the fusion of sensory information
- Sensory information;
- Sensor network architectures;
- Communication protocols.
General aspects of data fusion
- Data alignment – spatial, temporal, semantic and normalization;
- Calibration processes;
- Errors: inconsistency, noise, lack of information, outliers.
Data fusion based on statistical methods
- Fundamentals;
- Common data representation in Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA);
- K-Means Cluster;
- Kalman and Extended Kalman Filter;
- Mixture of Gaussians.
Data fusion based on machine learning and artificial intelligence
- Fundamentals;
- Database creation;
- Decision trees;
- Neural Networks;
- Deep Learning.
Performance evaluation strategie

Processing, Analysis & Coding of Digital Information – Semester – 5 ECTS

Models of digital representation of visual information in different modalities
- Multi-view, 3D, plenoptic, holographic, point clouds and omnidirectional;
- Dermoscopic, light-sheet, MRI, PET, CT;
- Thermal (infrared), multispectral.
Multidimensional information segmentation, identification and classification algorithms
- Computer vision solutions based on deep convolutional neural networks;
- Performance, optimisation and software tools.
Image, video and multidimensional signal coding algorithms
- Advanced techniques for image, video and multidimensional signal compression in international standards;
- Recent advances in the field of image and video compression using regions of interest;
- New coding paradigms based on machine learning.
Technological innovation in applications and integrated systems for digitalisation
- Coding, communication, processing, and analysis of visual information.

Robotics – Semester – 5 ECTS

Introduction
General robotics’ concepts
Artificial Intelligence in Robotics
Ongoing R&D projects in the partner institutions
Architectures for Robotic Agents
- Reactive, Deliberative, Hybrid;
- Belief, desire, and intentions;
- Cooperative architectures.
Perception in Robotics
- Proprioceptive and exteroceptive sensors;
- Computer vision and depth sensors applied to robotics;
- Sensor fusion techniques.
Localization and Mapping
- World model representation, generation, and update;
- Localization and mapping techniques;
- World exploration.
Actuation and Control in Robotics
- Kinematics and dynamics;
- Actuators and associated physical parameters;
- Robots and their simulation.
Navigation
- Navigation algorithms in known and unknown environments;
- Safety.
Collaborative Robots
- Impedance control;
- Robot safety features;
- Programming a collaborative robot

Smart Cyberphysical & IoT Systems – Semester – 5 ECTS

Overview of the Internet of Things
- Concepts;
- Architecture;
- Use Cases.
Embedded hardware systems for data acquisition
- Sensors;
- Actuators.
Low-power wireless communications infrastructure and protocols
System integration with the cloud
- Data transfer interfaces;
- Protocols.
Data Processing and Analysis for IoT
Security and Privacy in IoT
Distributed architectures for IoT systems
Anytime Anywhere IoT service’s
Smart IoT Ecosystems
Characterisation and evaluation metrics for Smart IoT solutionso

Energy Transformation – Semester – 5 ECTS

Framework and motivation
- Evolution of energy systems
- Security of supply
- Environment and climate
- Sustainability and Circular Economy
Structure of Energy Systems
- Classic monopolistic markets
- Need for promotion of independent generation
- Restructuration and liberalization processes
- Current market functions and agents
Characteristics of energy resources
- Fossil fuels
- Renewable resources
- Nuclear energy
Energy storage technologies
- Introduction and historic evolution
- Lithium-ion batteries and supercapacitors
- System performance forecast and optimization
- Modelling and digital simulation
- Limitations, advanced applications, challenges and future trends
Demand-side management and flexibility
- The concept of demand-side management
- Demand response as a flexibility option
- Energy communities and new forms of transaction
Digitalization and smart-grids
- Advanced monitoring
- Real-time control
- Proactive management

Complementary Skills in Digitalisation Engineering 1 – Semester – 5 ECTS

The following are used as training elements:

Technical-scientific missions with a minimum duration of two weeks (2.5 ECTS);
Laboratory rotations lasting a minimum of 2 weeks in institutions dedicated to digital innovation and knowledge creation (2.5 ECTS);
Summer/Winter Schools held. Diploma with ECTS, maximum of 5 ECTS, otherwise, upon Steering Committee assessment (2.5 ECTS);
Carrying out advanced courses “RUN SAP – Short Advanced Program” within the scope of RUN-EU, corresponding to 1 ECTS for each SAP of one week;
Publication of scientific articles – 5 ECTS for articles published in journals indexed in the SCI or international patent, and 2.5 ECTS for articles published in other journals or national patent;
Participation with presentation (oral or panel) of communications in national and international congresses (2.5 ECTS);
Other activities considered relevant by the Steering Committee may also be considered, with a maximum of 5 ECTS.

Complementary Skills in Digitalisation Engineering 2 – Semester – 5 ECTS

The following are used as training elements:

Technical-scientific missions with a minimum duration of two weeks (2.5 ECTS);
Laboratory rotations lasting a minimum of 2 weeks in institutions dedicated to digital innovation and knowledge creation (2.5 ECTS);
Summer/Winter Schools held. Diploma with ECTS, maximum of 5 ECTS; otherwise, upon Steering Committee assessment (2.5 ECTS);
Carrying out advanced courses “RUN SAP – Short Advanced Program” within the scope of RUN-EU, corresponding to 1 ECTS for each SAP of one week;
Publication of scientific articles – 5 ECTS for articles published in journals indexed in the SCI or international patent, and 2.5 ECTS for articles published in other journals or national patent;
Participation with presentation (oral or panel) of communications in national and international congresses (2.5 ECTS);
Other activities considered relevant by the Steering Committee may also be considered, with a maximum of 5 ECTS.

Outline Y3

Thesis – 120 ECTS

The student in this CU “Thesis” must undertake research work on Digitalisation Engineering subjects. The student will develop his research work, as an original contribution, in order to consolidate the knowledge and formulate new hypotheses, products, processes, or services. The student should develop its work autonomously, according to the Thesis Planning approved and supervised by the jury. The specific syllabus will depend on the topic chosen by the student.
During this period, the student will produce scientific papers published in scientific journals and conferences, which will be completed with the writing of a doctoral thesis that integrates all the research work performed and achieved results.

Entry Requirements

Minimum entry requirement, second class honours grade one (2:1) degree. Applicants with an ordinary degree with significant industry experience will also be considered.

Tuition Fee

€5,500 per annum

Application

Via TUS Graduate School. For more details contact graduatestudies@tus.ie