System design is the central topic of this course. We move beyond the methods developed in circuit design (although we shall have interest in those) and consider situations in which the functional behavior of a system is the first object under consideration.

Will robots take away our jobs? Will they have their own free will and – as some prophesy – have the power to take over the world? Or will robots offer a great contribution to our well-being and help us to achieve a sustainable future?

Many complex real-world problems can be solved using artificial intelligence. However, while combining artificial intelligence with robotics may offer positive implications for our society, it also raises ethical questions.

This course consists of four modules which can be followed in any order and accessed at any time. However, if you prefer to be part of a group with specific assignment deadlines and discussion forums, you can join and start the course on any of the following dates: June 25, September 3 or November 12, 2018.

In this course, you will be exposed to the potential societal and ethical impact of robots. We will touch upon the design principles that developers should adhere to and critically reflect on issues such as robot autonomy, consciousness, and intelligence.

At the end of the course, you will work with other participants to develop a groundbreaking solution that can be used by robot developers in the near future.

This course is a spin-off of the “Mind of the Universe” documentary series, created by the Dutch broadcasting company VPRO and professor Robbert Dijkgraaf, Princeton University. A number of universities in The Netherlands have used the open source material of the documentary series as a starting point to create similar learning experiences.

Modelling is about understanding the nature: our world, ourselves and our work. Everything that we observe has a cause (typically several) and has the effect thereof. The heart of modelling lies in identifying, understanding and quantifying these cause-and-effect relationships.

A model can be treated as a (selective) representation of a system. We create the model by defining a mapping from the system space to the model space, thus we can map system state and behaviour to model state and behaviour. By defining the inverse mapping, we may map results from the study of the model back to the system. In this course, using an overarching modelling paradigm, students will become familiar with several instances of modelling, e.g., mechanics, thermal dynamics, fluid mechanics, etc.

Physical and digital design skills are key to practitioners in art, design, and engineering, as well as many other creative professions. Models are essential in architecture. In design practice all kinds of physical scale models and digital models are used side by side.

In this architecture course, you will gain experience that will help and inspire you to advance in your personal and professional development. You will attain skills in a practical way. First, we will focus on sketch models for the early stages of a design process, then we will continue with virtual representations for design communication and finally more precise and detailed models will be used for further development of the ideas.

In the theoretical part of the course, you will learn about many different sorts of models: how architects use these and how they are essential in the design process.

The practical part of the course addresses a number of challenges. In small steps we will guide you through technical and creative difficulties in exciting, playful, and pleasant ways.

Distributed systems are the backbone of modern society but entail challenges in areas such as complexity and energy-use. Discover distributed systems from first principles, understand the architectures and techniques derived from them and explore examples of current practical use.

This course will provide learners with a fundamental understanding (theoretical and practical foundations) of how cloud, edge, and big data processing systems work and how they address common challenges for distributed systems such as performance, resilience, and scalability.

Modern IT infrastructure is built as distributed systems, an exciting concept that started with the first computers and evolved rapidly into its present form. From online video meetings to internet services, from social media platforms to online games, we all use and interact with distributed systems on a daily basis and increasingly depend on them. Designing and operating such large-scale distributed systems, however, is complex and typically involves making reasonable compromises. There are fundamental technical barriers as well as economic arguments why we cannot make these systems behave as if they were running on a single, perfectly reliable machine.

In this course, learners will be introduced to the essential functional and non-functional concerns of distributed systems and the common problems encountered while designing them, such as consistency, availability, elasticity, and scalability. A variety of practical solutions that have been established in the leading tech industry in recent years will be reviewed. These provide re-usable building blocks to create new large-scale applications. These recent developments, especially around cloud computing, large-scale data processing, distributed machine learning, and other fields are often not reflected in textbooks and are absent from many traditional curricula but are at the heart of this course.

The learning progress is assessed through a variety of different activities including quizzes, design exercises, experiments, and open questions, with peer review of other students’ solutions. In the final project, learners will design a distributed system based on the learners’ own experience and interests and describe the functional and non-functional properties of the system.

What You'll Learn:
Describe the principles of distributed systems.
Contrast distributed systems with other forms of computation (e.g., single machine computation, parallel computing).
Identify applications of distributed systems in science, engineering, business, and home use, and in particular the use of cloud and serverless applications, big data and graph processing applications, interactive and online gaming, etc.
Analyze and design core architectures, components, and techniques in distributed systems.
Solve practical problems related to modern uses of distributed systems.

The use of data to understand phenomena and evaluate designs and interventions in different disciplines is increasingly evident. As a result, engineers and other applied scientists frequently find themselves needing to collaborate in multidisciplinary fields when carrying out research to remain innovative.

This course will help you to become a successful multidisciplinary researcher in industry, non-profit, or academia, and be more efficient and successful as you will know where the pitfalls are! This course explains the fundamentals on how to plan and carry out state-of-the-art qualitative and quantitative research in different phases of an innovation or research project.

The course has been designed by a team of experienced, multidisciplinary researchers in education, engineering and research methodologies and will also feature experts in the field of research methodologies as guest lecturers. In the course you will be working towards creating a project plan for your research, giving you a head-start in your research project.

The interuniversity, interdisciplinary Leiden-Delft-Erasmus Center for Education and Learning is a leader in multidisciplinary technological research and innovation projects. Learning from leading experts in the field you will learn to apply the best practices in your own context.

How can ecosystems contribute to quality of life and a more livable, healthier and more resilient urban environment?

Have you ever considered all the different benefits the ecosystem could potentially deliver to you and your surroundings? Unsustainable urbanization has resulted in the loss of biodiversity, the destruction of habitats and has therefore limited the ability of ecosystems to deliver the advantages they could confer.

This course establishes the priorities and highlights the direct values of including principles based on natural processes in urban planning and design. Take a sewage system or a public space for example. By integrating nature-based solutions they can deliver the exact same performance while also being beneficial for the environment, society and economy.

Increased connectivity between existing, modified and new ecosystems and restoring and rehabilitating them within cities through nature-based solutions provides greater resilience and the capacity to adapt more swiftly to cope with the effects of climate change and other global shifts.

This course will teach you about the design, construction, implementation and monitoring of nature-based solutions for urban ecosystems and the ecological coherence of sustainable cities. Constructing smart cities and metropolitan regions with nature-based ecosystems will secure a fair distribution of benefits from the renewed urban ecology.

This course forms a part of the educational programme of the AMS Amsterdam Institute for Advanced Metropolitan Solutions and will present the state-of-the-art theories and methods developed by the Delft University of Technology and Wageningen University & Research, two of the founding universities of the AMS Institute.

Instructors, with advanced expertise in Urban Ecology, Environmental Engineering, Urban Planning and Design, will equip designers and planners with the skills they need for the sustainable management of the built environment. The course will also benefit stakeholders from both private and public sectors who want to explore the multiple benefits of restored ecosystems in cities and metropolitan regions. They will gain the knowledge and skills required to make better informed and integrated decisions on city development and urban regeneration schemes.

Infrastructures for energy, water, transport, information and communications services create the conditions for livability and economic development. They are the backbone of our society. Similar to our arteries and neural systems that sustain our human bodies, most people however take infrastructures for granted. That is, until they break down or service levels go down.

In many countries around the globe infrastructures are ageing. They require substantial investments to meet the challenges of increasing population, urbanization, resource scarcity, congestion, pollution, and so on. Infrastructures are vulnerable to extreme weather events, and therewith to climate change.
Technological innovations, such as new technologies to harvest renewable energy, are one part of the solution. The other part comes from infrastructure restructuring. Market design and regulation, for example, have a high impact on the functioning and performance of infrastructures.

Non-Linear Structural Modeling covers the basics of non-linearities in the Finite Element Method (FEM), considering static and stability (buckling) analyses, and practical application thereof applied to both aerospace and non-aerospace examples. Special emphasis is put on the implementation of these non-linearities in a FEM model and any issues that might arise from incorporating these

Are you an engineer, scientist or technician? Are you dealing with measurements or big data, but are you unsure about how to proceed? This is the course that teaches you how to find the best estimates of the unknown parameters from noisy observations. You will also learn how to assess the quality of your results.

TU Delft’s approach to observation theory is world leading and based on decades of experience in research and teaching in geodesy and the wider geosciences. The theory, however, can be applied to all the engineering sciences where measurements are used to estimate unknown parameters.

The course introduces a standardized approach for parameter estimation, using a functional model (relating the observations to the unknown parameters) and a stochastic model (describing the quality of the observations). Using the concepts of least squares and best linear unbiased estimation (BLUE), parameters are estimated and analyzed in terms of precision and significance.

The course ends with the concept of overall model test, to check the validity of the parameter estimation results using hypothesis testing. Emphasis is given to develop a standardized way to deal with estimation problems. Most of the course effort will be on examples and exercises from different engineering disciplines, especially in the domain of Earth Sciences.

This course is aimed towards Engineering and Earth Sciences students at Bachelor’s, Master’s and postgraduate level.

Part 2 of offshore hydromechanics (OE4630) involves the linear theory of calculating 1st order motions of floating structures in waves and all relevant subjects such as the concept of RAOs, response spectra and downtime/workability analysis.

Offshore Hydromechanics includes the following modules:1. Hydrostatics, static floating stability, constant 2-D potential flow of ideal fluids, and flows in real fluids. Introduction to resistance and propulsion of ships. Review of linear regular and irregular wave theory. 2. Analytical and numerical means to determine the flow around, forces on, and motions of floating bodies in waves. 3. Higher order potential theory and inclusion of non-linear effects in ship motions. Applications to motion of moored ships and to the determination of workability. 4. Interaction between the sea and sea bottom as well as the hydrodynamic forces and especially survival loads on slender structures.

The course treats the design of offshore mooring systems literally from the ground up: Starting with the anchor and its soils mechanics in the sea bed, via the mechanics of a single mooring line and system of lines. The course concludes by touching on other mooring concepts and the dynamic behavior of the moored object as a non-linear mechanical system.

This course makes students familiar with the design of offshore wind farms in general and focuses on the foundation design in particular. The course is based on actual cases of real offshore wind farms that have been built recently or will be built in the near future.

How can governments become more open and transparent, while simultaneously dealing with various challenges, such as data sensitivity? How can open government data be used to improve policy making? Which technologies are available to make governments more open and to use open government data?

Governments all over the world aim to become more open and transparent in order to establish closer ties with their constituents. However, opening government involves complex challenges and poses two major areas of concerns. First, many different stakeholders are involved and there are various dependencies between them, and second, the technologies that support open government are fragmented. In addition, it is unclear how different contexts should alter the best practices for open government.

This course explores the foundations and objectives of Open Government and examines current developments, including the opening and reuse of governmental data such as the release of data by governments in America and Europe.

This course will empower you, by helping you grasp the key principles surrounding open government.

You can become a more visible, effective and impactful researcher by sharing your research data and publications openly. In this course, you will learn the objectives, main concepts, and benefits of Open Source principles along with practices for open data management and open data sharing.

You’ll learn to establish links between publications data and methods, how to attach a persistent identifier and metadata to your results, and methods for clarifying usage rights. You will also discover ways to apply these principles to your daily research and adapt existing routines. Finally, you’ll uncover potential barriers to sharing research and discuss possible solutions.

This course will help you grasp the key principles of Open Science, with answers to questions like:

How can researchers effectively store, manage, and share research data?
What kinds of open access publishing are most effective?
How can researchers increase the visibility and impact of their research?
How can the use of social media contribute to the visibility and impact of research?
You will apply the topics of the course to a variety of case studies on Open Science adoption, which you will then discuss among fellow students. You will also be presented with a hands-on guide to publishing your research with open access. This will help you to apply Open Science principles in your daily work. It will enable you to implement and benefit from the Open Science policies that are currently being developed by governments and research institutions.

This course is aimed at professionals. Those who will see the most benefit include academic researchers at different levels: PhD students, postdoctoral researchers, and professors; researchers working for governments; researchers working for commercial enterprises; MSc and BSc students interested to learn about the principles of Open Science.

Deze cursus bestaat uit lesmodules te gebruiken in de bovenbouw van het Havo en het VWO met als onderwerp Optimaliseren in netwerken. Het materiaal is gemaakt door een kerngroep van vwo-docenten, aangevuld met universitaire medewerkers. Docenten kunnen er invulling mee geven aan het domein "Wiskunde in wetenschap" van het vak wiskunde D.

The goal of this course is to obtain knowledge of the origins of petroleum and gas. An overview is given on the conditions that are needed for oil and gas to accumulate in reservoirs. Moreover, techniques to find and exploit these reservoirs are highlighted. The focus always is on the task of the petroleum geologist during the different phases of oil and gas exploration and production. After an introduction to the course including typical numbers and historical developments, essential terms and concepts like biomolecules and the carbon cycle are explained.

This course is about solving complex problems. Our favorite problems are not just technically complex but also characterized by the presence of many different social actors that hold conflicting interests, objectives, and perceptions and act strategically to get the best out of a problem situation. This course offers guidance for policy analysts who want to assess if and how their analysis could be of help, based on the premise that problem formulation is the cornerstone in addressing complex problems. After this course, students would have obtained a theoretical insight into different models of decision-making processes, their implications in terms of supporting decision making and the potential roles that analysts; they can make a structured problem analysis in a complex situation, and can lay down their findings in an ‰"issue paper‰;" they know how to use a range of different methods and techniques to support attainment of these objectives; can formulate plans for a further analysis and closer examination, including the specification and the choice of possible mathematical models to be used.The completion of the practical part of this course will be an issue paper (written in pairs).

The course is a survey of models for analyzing and supporting design and decision-making in multi-actor settings. Participants: will learn to recognize the difference between games and decisions, and will identify their occurrence in public policy. will be able to apply a principled technique for resolving dilemmas through the appropriate selection of policies. will learn to formulate, design and communicate games and decisions. will learn to implement by computer (and otherwise logically analyze) games and decisions.