Engineering

The course contains four parts: managing logistics and operations system: value creation and strategic perspectives; designing industrial operations: products, processes, quality, and capacity; linkages to suppliers and customers: sourcing, purchasing, transportation, materials handling, and inventories; planning and controlling inventory and production in the supply chain: forecasting, long-term and short-term planning and control, and lean-based operations development.

The course provides a theoretical understanding and practical skills related to object-oriented programming. Practical skills will be learnt using the C++ programming language. The course enables students to tackle complex programming problems, making good use of the object-oriented programming paradigm to simplify the design and implementation process.

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.

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.