Digitalisation Engineering

PHD

study cycle by Polytechnic University of Leiria, Polytechnic University of Cávado and Ave and at the Technological University of the Shannon, in Limerick, Republic of Ireland

The PhD in Digitalisation Engineering has as its main objectives the training of highly qualified human resources 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 the School of Technology and Management of the Polytechnic of Leiria, in association with the Polytechnic Institute of Cávado and Ave and the Technological University of the Shannon, Republic of Ireland, 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

(updating)

School

Technology and Management

City

Leiria

Language

English, Portuguese

Type

Morning & Afternoon

Length

3 years

Vacancies

Notice

Notice 2024 2v

DGES certification

Link to registration

Objectives

Programme

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.

Study Plan

ECTS: 180

1. Year
Elective I and Elective II – available curricular units
2. and 3. years

1. Year
ID	Name	Semester	ECTS	Length
	Research Methodologies	1S	5	30 h
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	1S	5	.30 h
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. Recognizing improper conduct in research and procedures to follow in cases of misconduct in accordance with best practices.
	Elective I	1S	5	30 h
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 Visualization 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 Digitalization Engineering I
	Elective II	1S	5	30 h
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 Visualization 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 Digitalization Engineering II
	Seminar	2S	5	30 h
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	60 h
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.

Elective I and Elective II – available curricular units
ID	Name	Semester	ECTS	Length
	Data Analytics and Machine Learning	Semestral	5	30 h
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 and Remote Sensing	Semestral	5	30 h
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 and Data Visualization	Semestral	5	30 h
Distributed DBMS Architecture Distributed Database Design Query Processing and Decomposition Distributed query Optimization Transaction Management Distributed DBMS Reliability Parallel Database Systems Distributed object Database Management Systems Object Oriented Data Model Data visualization Tools for data visualization
	Cybersecurity	Semestral	5	30 h
Information Security and Cybersecurity Network Security Digital Forensic Analysis Malware Analysis Information Systems Risk Analysis Ethics, Compliance, and Human Factor Practical Cases
	Intelligent Control	Semestral	5	30 h
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	Semestral	5	30 h
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	Semestral	5	30 h
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 strategies
	Processing, Analysis and Coding of Digital Information	Semestral	5	30 h
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, optimization 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	Semestral	5	30 h
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 and IoT Systems	Semestral	5	30 h
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 Characterization and evaluation metrics for Smart IoT solutions
	Energy Transformation	Semestral	5	30 h
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 Digitalization Engineering I	Semestral	5	30 h
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 Digitalization Engineering II	Semestral	5	30 h
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.

2. and 3. years
ID	Name	Semester	ECTS	Length
	Thesis	Pluriannual	120	244 h
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

The following are eligible to apply to the cycle of studies:

Holders of a master’s degree or legal equivalent;
Holders of a foreign higher education diploma, granted after a 2nd cycle of studies, under the principles of the Bologna Process, by a State, which has subscribed this Process;
Holders of a foreign higher education diploma that is recognised as meeting the objectives of the master’s degree by the institution’s Technical and Scientific Council ;
Holders of a bachelor’s degree who have a particularly relevant academic or scientific curriculum that is recognized as certifying the skills to attend this cycle of studies by the institution’s competent board of school where the candidate has applied for;
Holders of an academic, scientific or professional curriculum that is recognized as certifying the skills to attend this cycle of studies by the institution’s competent board of the school where the candidate has applied for.

Accreditation

State: Accreditated
Number of years of accreditation: 6
Publication Date: 23/07/2024
Acreditação A3ES

More Information

	1.ª phase	2.ª phase	3.ª phase
Applications	27/09/2024 a 14/10/2024	28/10/2024 a 03/12/2024	02/01/2025 a 31/01/2025

For more information, please contact:

Professor Sérgio Manuel Maciel de Faria
sergio.faria@ipleiria.pt

Application Fee

General contingent: 60€

International contingent: 60€

Enrolment Fee

General contingent: 50€

International contingent: 100€

Tuition Fee

General contingent: 2750€

International contingent: 4000€