Engineering

This course is part of the Laurea Magistrale program. The course is intended for advanced level students only. Enrollment is by consent of the instructor. The course is divided in two modules. The aim of the first module is to provide knowledge about vehicle dynamics. Theoretical and numerical approaches are discussed to this end, as tools that allow students to predict the performance of cars in terms of longitudinal dynamics, lateral dynamics, handling, comfort, and stability. The aim of the second module is to provide the theoretical basis and the practical skills required to design embedded hardware and firmware compliant with industrial standards (safety, interoperability, maintainability). In addition, model-based design and automatic code generation using Matlab/Simulink is considered.

This course provides an introduction to the Social Study of Science and Technology. This is an area in which Edinburgh has longstanding strengths and which the course draws upon. The course examines some of the different ways of analyzing and understanding technology in society. It explores both the consequences of technical innovation for society and the ways technology is itself shaped by cultural, economic, political, and organizational factors. Students learn about a range of analytic perspectives on Technology in Society - drawing upon history, economics, and the sociologies of work, gender, and science & technology themselves. Students explore these issues in various settings - at work and in everyday life and in developing as well as developed countries. In the second part of the course, students apply these perspectives to particular technologies or issues, working together in student-centered learning. 

The course contains the learning materials, practices and case studies to develop the knowledge and skills of the students in the field of data science and its application in the real business/work world. The students learn how to apply analytical techniques and scientific principles to extract valuable information from business data for decision-making, strategic planning. This course covers practical contents of statistics, machine learning, information visualization, and data analysis techniques through python programming language and other tools.

This course emphasizes hands-on laboratory experience and teaches students research background, relevant theories, and basic laboratory techniques relevant to their field of study. Students formulate a research plan, implement it by conducting experiment-based research, and convey the results in scholarly presentations. Students submit a written research report at the end of the course.

This course provides research training for students through the experience of belonging to a specific laboratory at the University of Tokyo. Students carry out an original research project under the guidance of assigned faculty members. Through a full-time commitment, students will be able to improve their research skills by applying the basic principles and knowledge from the literature related to the research questions, and by developing the skills to collect, interpret, and critique data in order to resolve a research question or evaluate a design for a research project. At the conclusion of the program, students submit their final work (paper, presentation, report etc.) as instructed by their lab supervisors

In this course students acquire a broad knowledge base and develop analytical and critical thinking skills. Students actively participate in seminars, read assigned texts and research papers, and analyze research data. Students also discuss results obtained in their own experiments with peers and senior laboratory members.

This course nurtures student startups based on their hands-on user-centered design projects. The course proceeds like a 3.5-month pre-seed accelerator for experiential learning with an academic and theoretical foundation drawn from social-technical system design theories and principles. The practical venture building projects are aided with lectures, sprint workshops, panel discussions and weekly readings. Entrepreneurial students learn underlying factors and forces for decisions in the entrepreneurship process and the principles for designing products, processes and people organizations under extreme uncertainty and resource constraints. The course is supported by a large international network of 150+ entrepreneurs, venture capitalists, design and manufacturing experts around the world.

This course examines the role of data abstraction to solve problems. It discusses data structures, their characteristics, and implementation in object-oriented programming language. Topics include: arrays; recursions; lists; batteries; tails; trees; binary trees; binary search trees; balanced search trees; functions and hash tables; heaps; sorting algorithms; algorithms in graphs. 

This course explores the scientific basis for understanding energy management, both at global and local scales. It discusses the energy resources available worldwide, how we consume energy today, and the pros, cons, and limitations of each form of energy production. This course examines the criteria that should drive energy management and energy transition at a global scale, as well as the current geopolitical and economic scenarios that impact this process.

A research project that assigns students to expert professors in their proposed research topic. The course takes the student's research capabilities to a more professional level. This can be most closely compared to what is called a supervised research project in American universities.