Imperial College London

This course teaches students to use MATLAB for data processing, visualization, simulation, and analysis; apply probability models, estimate their parameters and test their fit to data; apply reliability theory to devices and networks; and perform predictive modelling tasks using regression and time series analysis.

This course teaches students to appraise engineering with alloys, and evaluate multi-objective engineering design problems (cost, temperature, performance – e.g. creep, fatigue, strength, processability, light weighting, material costs & lifecycle). Students discuss approaches to engineering design and lifing, where failure and optimisation of alloys dominate function (drawing in ideas of process-microstructure properties) in solid stage metal components and consider the science of alloys as a microstructure system with an engineering goal. 

This course introduces the underlying molecular basis of cellular and sub-cellular processes in cells, with special emphasis for engineers. The course introduces students to some of the physiological concepts and systems that are important application areas for technology in the field of bioengineering and develops the practical laboratory skills to use the technologies introduced in the lectures.

This course teaches students to acquire the knowledge and ability to design and analyze complex mechanical systems. This teaches students about the design process, product design specification, computer-aided design, design for manufacturing, equivalent stresses and failure criteria, transmissions and machine elements, prototyping, fatigue, shaft design, practical workshop skills, conceptual design, motors and batteries, design in plastics, ergonomics, and intellectual property.

Topics covered provide students with a comprehensive knowledge, theoretical and applied, to design and realize a full mechatronic system. The course content includes an overview of Control systems, modelling of dynamic systems, Laplace Transforms, Root locus, Steady state errors, final value theorem, Frequency response analysis, Bode diagrams, Compensation, and PID Controllers.

This course introduces students to advanced numerical methods for the solution and optimization of both linear and nonlinear systems, so that they are able to apply them in real chemical engineering problems. Students learn about optimization theory and how to formulate optimization models for linear and nonlinear problems, select an appropriate solution method, and compute a numerical solution. The numerical software tool for this course is GAMS.

This course includes topics such as user-centric design, user study design, data analysis, and verbal and non-verbal robot behavior. Additionally, the course explores several human-robot interaction applications such as healthcare, education, and in-home robots.