Engineering

This is an advanced course in linear and logistic regression, which expounds on the knowledge gained in introductory mathematical statistics courses. It covers matrix formulation of multivariate regression, methods for model validation, residuals, outliers, influential observations, construction and use of F- and t- tests, likelihood-ratio-test, confidence intervals and prediction, and applied implementation of various techniques in R software. Students also consider correlated errors, Poisson regression, multinominal and ordinal logistic regression. The first part of the course expands on previous study of linear regression to consider how to check if the model fits the data, what to do if it does not fit, how uncertain it is, and how to use it to draw conclusions about reality. The second part of the course explores logistic regression, which is used in surveys where the answers follow a categorical alternative pattern such as “yes/no,” “little/just fine/much,” or “car/bicycle/bus.” Students describe differences between continuous and discrete data, and the resulting consequences for the choice of statistical model. Students learn to give an account of the principles behind different estimation principles, and describe the statistical properties of such estimates as they appear in regression analysis. The interpretation of regression relations in terms of conditional distributions is studied. Odds and odds ration are presented, and students describe their relation to probabilities and to logistic regression. Students formulate both linear and logistic regression models for concrete problems, estimate and interpret the parameters, examine the validity of the model and make suitable modifications, use the model for prediction, utilize a statistical computer program for analysis, and present the analysis and conclusions of a practical problem in a written report and oral presentation. The course makes use of lectures, exercises, computer exercises, and project work.

This course gives a general introduction to embedded system design which can be implemented using System-on-Chip technology. This kind of embedded systems contains both hardware and software components and therefore a hardware/software co-design is emphasized. The course gives a basic knowledge on specification methods, design representations (computational models) as well as related design methods. Special emphasis is placed on interface synthesis and low-power design methods.

This course introduces key topics on i) thermodynamic states and properties, ii) thermodynamic systems and their applications, iii) the 1st and 2nd law of thermodynamics, iv) power systems, and v) refrigeration systems. It covers energy conservation, reversible and irreversible processes, and thermodynamic efficiencies. Students learn how to design, analyze, and improve thermodynamic systems based on key principles which are introduced in this course.

This course offers a study of the main principles of materials science and engineering including the relationship between structure, chemical bonding, properties, processing, and applications. It focuses on the primary group of materials including ceramics, metal, polymers, and composites. Other topics include: crystalline defects and solid solutions; diffusion; equilibrium phase diagrams; mechanical properties; heat treatments.

This course examines the full spectrum of energy systems used in modern society, including fossil fuels and renewable energy sources, with attention to power generation technologies, energy security, and sustainability challenges. Topics include energy conversion, utilization, and storage for renewable technologies such as wind, solar, biomass, fuel cells, and hybrid systems, grounded in fundamental thermodynamics concepts. The course also explores energy-efficient technologies in buildings, products, manufacturing, and infrastructure, as well as the environmental and social impacts of energy use. Through case studies and discussion, the course addresses debates and misconceptions surrounding sustainable energy systems, energy entrepreneurship, and the role of renewable energy in mitigating climate change and supporting contemporary lifestyles.

Blue Engineering focuses on ecological and social responsibility. The course facilitates creative and interdisciplinary debates on the issues posed by technology in society and in nature. It enables students to network beyond their university and even beyond national borders while exchanging ideas and getting ready to act. As the course focuses on sustainability, topics such as technology assessment, engineers' responsibilities, neutrality of technology, plastics, and gender/diversity. 

Lithium-ion (Li-ion) batteries have revolutionized portable electronics; from mobiles to laptops, Li-ion batteries are omnipresent within modern society. Furthermore, we are now seeing a global shift within the automotive industry towards the adoption of electric vehicles, predicted to be a trillion £ market by 2050. This course requires no prior knowledge of battery technology and cover all major aspects, from fundamental operation through to commercial application. This includes tours of cutting-edge research facilities, external speakers from the likes of NASA and perspectives covering: government policy, industrial production, project management, commercial business and marketing.

This interaction between actors in the energy market creates opportunities to use energy more efficiently and reduces the environmental impact of the energy system. It is therefore important to be able to understand the limitations and possibilities of the components and to optimize their usage within the energy system. This course provides engineering expertise regarding energy processes and components within energy systems, and provides the tools needed to argue, judge, and evaluate possible solutions. Prior knowledge of thermodynamics is required.