**Climate Change and Sustainability**  

The reality of climate change is imperative for societies to innovate and action solutions that can advance the world to a more sustainable future. Bringing digital technologies and sustainability goals together is logical, and necessary, and today Digital Twins are already having a radical impact on sustainability. By its nature, digital technologies are adaptable and responsive to innovation – but technology providers do not have a monopoly on ideas. This challenge seeks new ideas, and original thinking, to apply Digital Twin technologies to sustainability goals.  

**Digital Twins**  

Digital twins are virtual counterparts of tangible assets, providing real-time data, optimisation, predictive analytics, and alerts. The significance of DT isn't confined to engineers or facility managers, it's equally valuable for end-users and property owners. A pivotal area where digital twin technology shows promise is in real-time sustainability monitoring.  

Digital Twins can significantly improve the design, construction, and operation of assets of all types. It can improve efficiency by monitoring performance in real time, including carbon outputs. It is envisaged that Digital Twins should simulate entire systems or interlinked assets to simulate future effects of climate or citizen actions. They should enable society to do more with less, to utilise resources better, longer, and to recycle where replacement has been the norm, to align engineering data with reality, geospatial, IOT and other operational data. By means of IoT data, Digital Twins should allow AI and machine learning to better analyse performance and improve efficiency.  

If the above is true – what are the opportunities yet unseen, what has not been envisaged, and where are the next true innovations? Recognising that there is often doubt and scepticism when innovation challenges long-held thinking, submissions should consider how societies can facilitate those who would otherwise be sceptical to be part of that change as data proliferation increasingly becomes more widespread and more important to the goals of optimal sustainability.

Member universities may require additional information from applicants. Please read the application information section carefully and include any extra required information in this section.

Other

Motivation letter is a personal statement and it might include a brief introduction of yourself, explanation why are you interested in the learning opportunity you are applying to, any relevant experience or skills that you have, your goals and how this opportunity will help you achieve them, This document should not be more than 400 words.

Motivation Letter

Transcript of Records is an official document issued by your home university. It specifies the name of the institution, the academic programme you are enrolled in, the list of courses taken up until the current period, the exams or tests you passed, the grades and the credits you earned. 

Transcript of Records

Kaunas University of Technology

Urban Digital Twins for Sustainability

How can innovative technologies be applied for improvement of sustainability indicators within the built environment?

Entrepreneurship, technology and innovation

Critical and innovative thinking

Inter-personal skills

Media and information literacy

Positioned at the intersection of challenge and opportunity, this module is designed to future-proof students with the competencies and skills required to leverage digitalisation, address societal challenges and provide unique value to organisations. 

Through practice-led and experiential teaching, students are exposed to practical ideas and live cases that reflect the realities of contemporary work. Students will be presented with a real-world challenge that reflects the realities of industry and society in a way that allows them to connect learning and experience. This unique learning experience will equip learners with the knowledge and skills necessary to prepare them for the realities of the future world of work.

Challenge Based Learning (CBL) is a powerful framework that encourages learners to problem solve though real world application. Students will have a unique opportunity to engage with industry leaders and company representatives over the course of this learning experience. This module is designed to offer a transformative learning experience allowing students to embrace their communicative, critical thinking, creative thinking and problem-solving skills and develop an innovation mindset.

CV (Curriculum Vitae) is a document that summarizes your educational and professional history. It must provide a detailed overview of your academic and work experience, skills, achievements, and qualifications.

Dublin City University

EcoTech Challenge – Pioneering Innovative Technologies for a Sustainable Future

Leveraging cutting edge technologies to support a greener future

Circular economy

It is important to understand and assess the suitability of machine learning techniques for use with your data. 

The suitability of many techniques used in machine learning relies on several assumptions which are not always adhered to. The implications of understanding how these techniques estimate the weights in the various models are significant when concluding the results. 

In this course, you will examine Bayesian Estimation and the Monte Carlo Markov chain algorithm. You’ll also implement its algorithm for a regression problem.

- This course will show students why Bayesian Estimation is a good approach.

- You will understand the difference between a frequentist and Bayesinist.

- This course will also introduce the Monte Carlo Markov chain algorithm.

- We will learn how to implement this algorithm in order to solve a regression problem.

Bayesian Estimation

Machine Learning Techniques

The course is aimed at IT professionals in employment in the Republic of Ireland registered companies. To qualify for direct entry they must have a Level 8 Honours Degree (2.2) or higher in Computer Science, Computing, Computer Applications or a related discipline. Applicants without these entry requirements (e.g., Level 7 degree or lower than an Honours 2.2 in a Level 8 degree) may be considered if they can demonstrate previously obtained competence equivalent to the entry requirements.

It is important to understand and assess the suitability of machine learning techniques for use with your data.  

The suitability of many techniques used in machine learning relies on several assumptions which are not always adhered to. The implications of understanding how these techniques estimate the weights in the various models is significant when concluding the results.  

In this course, you will examine Maximum Likelihood Estimation (MLE). It is one of the most important parameter estimation techniques in statistical modelling. We outline why this is the case and we will show you how they can derive theoretical estimates from first principles.

In this course we will outline Maximum likelihood estimation is one of the most important parameter estimation techniques in statistical modelling.

Upon completion you will understand what a maximum likelihood estimator is and how to implement it to estimate a parameter for the mean and regression models.

You will also be able to outline the optimal characteristics of a maximum likelihood estimator.

Maximum Likelihood Estimation

(MLE)

The course is aimed at IT professionals in employment in the Republic of Ireland registered companies. To qualify for direct entry they must have a Level 8 Honours Degree (2.2) or higher in Computer Science, Computing, Computer Applications or a related discipline. Applicants without these entry requirements (e.g., Level 7 degree or lower than an Honours 2.2 in a Level 8 degree) may be considered if they can demonstrate previously obtained competence equivalent to the entry requirements.

In this course, we will look at how the General Data Protection Regulations (GDPR) contradicts the principle of blockchain data immutability and how the absences of a central governance system can cause serious problems, especially in the financial sector.

We will discuss some blockchain security issues such as crypto-exchange hacks. Furthermore, we will explore cultural issues such as Blockchain’s reputation and how crypto has a negative image that is surrounded by fraudsters, hackers.

Finally, we will discuss the lack of awareness, cooperation and understanding of how blockchain works, especially with small and medium-sized companies.

In this course, we will discuss governance issues such as the lack of policies and regulations faced by blockchain,  blockchain privacy and security issues such as crypto-exchange hacks.

We will explore Cultural Issues such as Blockchain’s reputation and lack of awareness and understanding about how Blockchain works for smaller enterprises.

Upon completion of this course, you will be able to; identify the major blockchain obstacles hindering its emergence, explain each obstacle and how it affects the adoption of blockchain technology, outline ways to overcome blockchain obstacles and reach its full potential.

Obstacles Facing Blockchain Emergence

(Blockchain)

In this course, we will detail some of the blockchain stakeholders such as; governments, industry bodies, venture capitalists, educators and developers.

Additionally, we will explore the top pioneers as well as companies and countries that are leading the blockchain revolution.

Blockchain technology is in a period of rapid change and intense development. We are going to discuss some of the innovative ideas and services offered by blockchain. Furthermore, we will explore blockchain with AI and what lies ahead for the future of blockchain.

We will explore the top pioneers, companies and countries that are leading the blockchain revolution. 

We will discuss some of the innovative ideas and services offered by blockchain.

Finally, we will explore Blockchain with AI and what lies ahead for the future. 

Upon completion of this course, you will be able to identify the key blockchain stakeholders, discuss and identify the top pioneers and companies leading the blockchain revolution, outline the potential of blockchain technology and its agenda for the next era.

Blockchain Leadership for the Next Era

Using a Challenge-Based-Learning framework the module will provide students with a range of practical learning opportunities demonstrating how chemistry and bioprocessing can be key enablers of circularity across different industrial sectors. Students will engage with industry partners to learn more about the chemical and bioprocessing sectors, the current state of the art in sustainability and circularity, and will tackle current challenges to these sectors.

Bioprocessing for the Circular Economy

Learn how bioprocessing can be a key enabler of circularity across different industrial sectors.

Intra-personal skills

In this course, we will analyse the technical problems such as scalability and transaction speed faced by blockchain. Furthermore, we will study different researches that were carried out on how to make blockchains faster and more scalable without compromising on security.

In order for blockchain technology to reach its full potential, it needs to be interoperable. We will analyse some blockchain interoperability solutions and project initiatives. Finally, we will discuss other issues, such as energy consumption and lack of talent in the blockchain industry.

Upon completion of  this course,  you will be able to; identify the major blockchain implementation challenges and issues, explain each blockchain implementation issue, understand the impact of the challenges on the adoption of blockchain technology, and outline some existing initiatives trying to address the blockchain challenges

Blockchain Challenges and Issues

# **The Challenge**

A fundamental Machine Learning challenge can develop learners in several ways:

Career Opportunities: Machine Learning is a rapidly growing field, and there is a high demand for professionals with expertise in this area. By acquiring knowledge in fundamental Machine Learning concepts, learners can open up new career opportunities and enhance their employability. Knowing that there is a demand for their skills can be a strong motivating factor for individuals considering learning Machine Learning.

Practical Application: Machine Learning has numerous real-world applications across various industries such as healthcare, finance, marketing, and more. Learning the fundamentals of Machine Learning can help learners understand how it can be applied to solve complex problems and make predictions. This practical aspect can be highly motivating for individuals who are interested in using technology to create impactful solutions.

Innovations and Advancements: Machine Learning is at the forefront of technological advancements and innovations. By studying the fundamentals, learners can gain insights into cutting-edge research and developments in the field. This exposure to exciting advancements and breakthroughs can inspire and motivate learners to explore Machine Learning further.

Problem-Solving and Critical Thinking: Machine Learning involves analyzing data, identifying patterns, and creating models to make predictions or decisions. The process of learning Machine Learning requires learners to develop problem-solving and critical thinking skills. Engaging in challenging problem-solving exercises and seeing the results of their efforts can be highly motivating for learners.

Machine Learning

An overview of the tools, techniques and purpose of machine learning.

This course is aimed at computing and engineering professionals who have an interest in the area of machine learning and wish to gain practical skills with sufficient theory to have a grasp of the principles behind the algorithms. This course assumes a knowledge of computer programming in Python, statistics, basic calculus and linear algebra.

- In this course, we will examine Reinforcement learning in action.
- You will gain access into the best context for the application.
- Upon completion of this module, you will be able to understand the fundamental ideas behind reinforcement learning and apply reinforcement learning approaches using publicly available resources.

Reinforcement Learning

Machine Learning Approach

- In this course, we will go through the basics of business ethics.
- We will focus on how this discipline relates to our everyday life and choices, and on the philosophical foundations of this discipline.
- We will develop a solid definition of ethics.
- Upon completion of this course, you will be able to  define what “responsibility” means in the business context.

- Identify the main misbehaviours in the context of business

Philosophical Frameworks for Business Ethics

Identify the role that business ethics plays within a working environment with Dublin City University.

For computer systems to learn from experience they need information from the outside world structured in a way that is better suited to how they learn. To do so effectively, we must explore different means of representing common types of information such as images, video, sensors and text. In this course, we will explore useful ways of doing exactly that.

In this course, we will examine some practical examples of data representation as they can occur with real-world machine-learning tasks.

We will examine suitable feature representations for images, text and time series

We will develop an appreciation for the data representation challenge and the importance of giving attention to this stage of the machine learning development process.

Upon completion of this module, you will be able to Apply a range of concepts to explore different representations of data. Understand the limits and breadth of Machine Learning capabilities and Evaluate different representations of data for different purposes.

Data Representation Real World

(Data analytics)

This informative course will take you through the main themes and concepts of ethics within a business context. You’ll take the perspective of the individual within a business and will explore what ‘responsibility’ means within a business context. You’ll then cover the main misbehaviours that can happen within a business and ways in which to avoid them.

You’ll discover that ethics is not something that you “add” to your actions, it is within the action itself.

In this course, we will explore how individuals behave when they’re at work in a company.

We will answer questions like: What is responsibility? When can we say that someone is responsible for a decision or for a particular course of action?

We will distinguish between different kinds of actions that a person can perform in his or her everyday life and, specifically, in the context of business.

We will focus on certain misbehaviours in business, presenting many examples. 

Upon completion of this course, you will be able to Define what “responsibility” means in the business context and Identify the main misbehaviours in the context of business.

Individual Ethics in Business

(Business Ethics)

This informative course will take you through the main themes and concepts of ethics within a business context. You’ll reflect on the relationship between a business and wider society. You’ll explore different ways of dealing with cultural diversity and conclude with an essential look at sustainability. You’ll learn about the UN Global Compact and the UN Sustainable Development Goals and how they relate to business

- In this course, will examine the different theories on the relationship between business and society.
- We will explore the areas of cultural diversity and sustainability and how they relate to  business.
- Upon completion of this course you will be able to  reflect on the relationship between business and society. Describe what is cultural diversity and its relevance for business ethics and explain what the Sustainable Development Goals are and why they are important for business.

Sustainable Business and Cultural Diversity

Global citizenship

In this course, we will explore some blockchain societal impacts and discuss the findings of a study by Stanford University.

We will detail five different sectors by discussing the Stanford study findings and highlight some initiatives as well as the challenges facing the adoption of blockchain in these sectors.

Upon completion of this course, you will be able to Identify the societal sectors impacted by blockchain technology 

Evaluate the impact of blockchain on each of the outlined societal sectors.

Summarise the Stanford research study findings on the impact of blockchain technology.

Discuss the blockchain primary benefits on impacted sectors.

Blockchain Promises and Societal Impacts

- In this course, will examine the different theories on the relationship between business and society.
- We will explore the areas of cultural diversity and sustainability and how they relate to  business.
- Upon completion of this course you will be able to  reflect on the relationship between business and society. Describe what is cultural diversity and its relevance for business ethicsand explain what the Sustainable Development Goals are and why they are important for business.

Managerial Ethics in Business and Leadership Theory

Neural networks are one of the most exciting approaches to machine learning currently available to practitioners. Learn how simple computational units coarsely modelled on the neurons in biological brains can give rise to super-human levels of recognition and performance in a wide variety of machine learning tasks.

In this course, we will examine Neural Networks in terms of their most fundamental concepts and ideas.

We will examine some important ideas regarding training such networks

Upon completion of this course, you will be able to Describe how machine learning has evolved. Understand the fundamental ideas behind neural networks and Utilise a neural network approach for a machine learning challenge.

Neural Networks - Machine Learning

(Machine Learning)

This course is aimed at computing and engineering professionals who have an interest in the area of Machine Learning and wish to gain practical skills with sufficient theory to have a grasp of the principles behind the algorithms. This course assumes a knowledge of computer programming in Python, statistics, basic calculus and linear algebra.

- In this course, we will examine  fundamental data types which lend themselves particularly well to certain well-known machine learning tasks.
- We will look at  feature sets - which are particularly useful and incredibly important for effective machine learning.
- We will develop solid foundations for understanding data and look at some real world examples.
- Upon completion of this course, you will be able to Apply a range of concepts to explore different representations of data. Understand the limits and breadth of Machine Learning capabilities and Evaluate different representations of data for different purposes.

Data Representation Fundamentals

(Data Analytics)

The course is aimed at IT professionals in employment in the Republic of Ireland registered companies. To qualify for direct entry they must have a Level 8 Honours Degree (2.2) or higher in Computer Science, Computing, Computer Applications or a related discipline. Applicants without these entry requirements (e.g., Level 7 degree or lower than an Honours 2.2 in a Level 8 degree) may be considered if they can demonstrate previously obtained competence equivalent to the entry requirements.

It is important to understand and assess the suitability of machine learning techniques for use with your data.

The suitability of many techniques used in machine learning relies on several assumptions which are not always adhered to. The implications of understanding how these techniques estimate the weights in the various models is significant when concluding the results.

In this course, you will examine the Generalised Linear Model (GLM). GLMs can be very useful as we can apply the concepts learned in linear regression to variables that come from the exponential family such as counts or binary variables.

In this course we will look at how GLMs can be used, with concepts that we learned earlier in Linear regression for variables that come from the exponential family of distributions.

On completion of this course, you will be able to recognise if a GLM is appropriate for your particular model and if so what family and link functions should be used.

Generalised Linear Models

(GLM)

**Climate Change and sustainability**  
The reality of climate change is imperative for societies to innovate and action solutions that can advance the world to a more sustainable future. Bringing digital technologies and sustainability goals together is logical, and necessary, and today Digital Twins are already having a radical impact on sustainability. By its nature, digital technologies are adaptable and responsive to innovation – but technology providers do not have a monopoly on ideas. This challenge seeks new ideas, and original thinking, to apply Digital Twin technologies to sustainability goals.  
  
**Digital Twins**  
Digital twins are virtual counterparts of tangible assets, providing real-time data, optimization, predictive analytics, and alerts. The significance of DT isn't confined to engineers or facility managers, it's equally valuable for end-users and property owners. A pivotal area where digital twin technology shows promise is in real-time sustainability monitoring.  
Digital Twins can significantly improve the design, construction, and operation of assets of all types. It can improve efficiency by monitoring performance in real-time, including carbon outputs. It is envisaged that Digital Twins should simulate entire systems or interlinked assets to simulate future effects of climate or citizen actions. They should enable society to do more with less, to utilise resources better, longer, and to recycle where replacement has been the norm, to align engineering data with reality, geospatial, IOT and other operational data. Utilising IoT data, Digital Twins should allow AI and machine learning to better analyse performance and improve efficiency.  
If the above is true – what are the opportunities yet unseen, what has not been envisaged, where are the next true innovations? Recognizing that there is often doubt and skepticism when innovation challenges long-held thinking, submissions should consider how societies can facilitate those who would otherwise be skeptical to be part of that change as data proliferation increasingly becomes more widespread and more important to the goals of optimal sustainability.

How can innovative technologies be applied for real-time monitoring, assessment, and improvement of sustainability indicators within the built environment?

Member university

Dublin City University

Member university

Dublin City University

Learning opportunities of Dublin City University