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United in Manchester International Summer School

Specialist subjects (morning sessions)

You will gain a solid grounding in one of four key science and engineering disciplines as part of the International Summer School.

Find out more about the streams available. You must choose only one of these streams.

Stream 1: Chemical sciences

A female student in a chemistry lab

The Chemical Sciences stream is co-organised by the Department of Chemistry (CHEM), the Department of Chemical Engineering and Analytical Science (CEAS) and the Department of Earth and Environmental Sciences (EES).

The stream consists of 9 morning sessions, covering 5 topics. Each session is 3 hours.

Session details

Energy quantization

Evidenced by live emission spectroscopy showing lines, (proof of quantization), also fireworks emission spectroscopy, implications of quantization: wave behaviour (strings, plates, then 3D waves: orbitals), orbital overlap, MO of O2 and N2, hetero-diatomics, S2 chemiluminescence. Follow up with some workshop questions relying on MO ideas.

  • Delivery: Demo lecture and workshop
  • Duration: 1 session
  • Lead Department: CHEM

Mini-design projects to showcase the basics of Chemical Engineering

One session on mass and energy balances, one on reaction engineering, and one on how to design a reactor. Additionally, guided tour of the pilot plant at the James Chadwick building.

  • Delivery: Workshops and Pilot Plant tour
  • Duration: 3 sessions
  • Lead Department: CEAS

Sustainability Challenge

Introducing you to the challenge of sustainability. You will have to work against the clock in interdisciplinary groups to develop plans for a new campus, while global responses to climate change triggered a series of ‘game changing’ interventions.

  • Delivery: Lecture/workshop
  • Duration: 1 session
  • Lead Department: CEAS

Sustainable feedstocks and Industrial biotechnology

Survey of common materials currently sourced from petrochemicals. Discussion of alternative renewable feedstocks to replace petrochemical feedstocks: sugars, lignin, biomass, CO2. Apply basic chemical principles to the harvesting and manipulation on non-traditional chemical feedstocks. Sustainable catalysis and biocatalysis.

  • Delivery: Lecture/workshop + lab session
  • Duration: 2 sessions
  • Lead Department: CHEM

Urban water quality management

You will learn the theory and practice of methods and approaches used by regulatory bodies, environmental consultants and research scientists to assess impacts on water quality of rivers subject to the effects of urbanisation. You will be introduced to the sources of anthropogenic stress including pollution, river re-engineering and catchment modification. You will then carry out assessments of chemical and biological water quality of urban aquatic sites in Manchester. The session will include a presentation from an environmental consultancy company on their role in water quality management and remediation.

  • Delivery: Field trip, lab analysis, lecture
  • Duration: 2 sessions
  • Lead Department: SEES

Stream 2: Computer science

A male student sat looking at data graphics on a screen

The computer science stream contains the following course components:

Unit 1: Introduction to Artificial Intelligence

Unit summary

This is an introduction and short course in Artificial Intelligence (AI): It will present what is AI; the brief history of AI; and how to use AI techniques to solve basic application problems such as robot localisation. The course presents AI from a probabilistic viewpoint (the modern approach) rather than from a logic reasoning viewpoint (the traditional approach). It emphasizes using probability theory as a reasoning tool to solve AI problems with uncertainties.

Teaching formation

The teaching formation includes the lectures, example classes, and lab exercises. The lectures will present the main theoretical ideas needed to tackle AI problems; the examples classes will reinforce these through paper-and-pencil exercises, and the labs will involve the use of programs to solve them.

Learning outcomes

By the end out this course unit you will:

  • understand the overall historical development of the subject and the major research areas and the overall historical development of the subject;
  • understand the basic AI reasoning method based on probability theory;
  • be able to apply AI methods to solve some application problems as the example of localising a mobile robot.

Pre-requirements

  • Basic knowledge about probability theory.
  • Basic knowledge and experience in computer programming.

Unit 2: Introduction to Machine Learning

Unit summary

This is an introduction and short course in Machine Learning: It will present 1) what are the basic problems and concepts in Machine Learning; 2) introduce several basic machine learning methods and algorithms such as linear classification/regression, logistic regression, K-Nearest Neighbour Classifier, Support Vector Machine, Clustering algorithms; 3) how to use machine learning to solve some simple application problems.

Teaching formation

The teaching formation includes the lectures and lab exercises. The lectures will present the main theoretical ideas and algorithms needed to learn from data and solve application problems, and the labs will involve how to code and use the algorithms to solve some simple application problems.

Learning outcomes

By the end out this course unit you will:

  • understand the problems that machine learning can be used to solve;
  • understand the basic concepts of Machine Learning;
  • be able to write some very simple machine learning algorithms and apply such algorithms to solve some simple application problems.

Pre-requirements

  • Basic knowledge about university mathematics and probability theory.
  • Basic knowledge and experience in computer programming.

Stream 3: Robotics

Two female students conducting research at the Photon Science Institute

Unit 1: Control and Robotics

Unit summary

This is an introduction to control and robotics. The main objective of this stream is to present Introduction to Robotics. Therefore, students during this summer school will study several applied and theoretical modules to understand a basic robotic system. All the modules will be delivered at the lab hence students will involve directly with hardware and will learn required software for the robotic systems.

Teaching formation

The teaching format includes lectures and lab exercises with many examples implemented by real robots. Mona is an open-source mobile robot which has been developed at the School of Electrical and Electronic Engineering for research and education.  Mona will be the robotic platform for teaching control and robotic concepts.

Learning outcomes

  • You will understand historical development of robotics;
  • You will understand the basic control systems, e.g. PID controller, theory and application on real robots;
  • You will be able to program and control a mobile robot that can avoid obstacles and follow a pre-define path.

Pre-requirements

  • Basic knowledge about programming.
  • Basic knowledge about mathematical solution.

Unit 2: Mechatronics Design

Unit summary

This is an introduction to mechatronics design. It will contain low-level design and development of a robotic system. The course will provide: 1) introduction to PCB design, 2) mechanical CAD design, and 3) applied control (LabVIEW programming).

Teaching formation

The teaching format includes lectures and lab exercises. Students will design PCBs using Altium designer software. In the second part of the course, student will study how to design CAD models using SolidWorks software. In the last part of the course, student will use LabVIEW to programme and compile basic control routines.

Learning outcomes

  • You will learn how to design PCBs;
  • You will learn how to use SolidWorks software to design CAD model;
  • You will learn to use LabVIEW in basic level and compile examples.

Pre-requirements

  • Basic knowledge about electronic components and physics.
  • Basic knowledge about control systems.

Stream 4: Mathematics

Maths academic writing an equation on a chalk board

The Mathematics stream is organised by the Department of Mathematics. The first half will be devoted to probability, statistics and data analysis. The second half will be a short course in coding theory.

The stream consists of 15 morning sessions cover these 2 topics. Each session is 3 hours.

Unit 1: Probability, statistics and data analysis

Today we live in an information-rich world where data plays an increasingly important role. Data analysis requires a strong background in Probability and Statistics. The aims of the course are to help students develop a knowledge of basic statistical concepts and methodology, analyse and compare statistical properties of estimators and tests, conduct exploratory data analysis and statistical inferences, and use the statistical computing software R to carry out simple data analysis.

  • Delivery: Lectures, tutorials + lab session followed by a final exam
  • Duration: 7.5 sessions

Unit 2: Coding theory

Data processing on an industrial scale, which began about 70 years ago, gave rise to the problem of guarding information from errors. Early solutions to this problem, such as the Manchester Code which is still used in TV remotes, could help spot the errors, but Richard Hamming’s code was the first to automatically correct them. Error-correcting codes allowed pictures to be transmitted from deep space using a tiny amount of power, and were then adapted to CDs, computer networks and WiFi – they are a reason why the smartphones can be so thin. Error correction, used in literally all data applications, is based on rigorous ideas and methods from pure mathematics – algebra, geometry, combinatorics, logic, which will be explored in the course and are related to cutting-edge mathematical research in group theory and representation theory.

  • Delivery: Lectures, tutorials, interactive quizzes, a short project and a formal assessment, discussion and feedback
  • Duration: 7 sessions

Stream 5: Aerodynamics

An aeroplane from below

Unit 1: Computational Fluid Dynamics

Unit summary

The objective of this course is to provide an overview of the computational fluid dynamics (CFD) tools available for engineers to predict aerodynamic forces acting on bodies in flow. We will focus on turbulence modelling and simulation which is fundamental to aerodynamics particularly for geometries involving flow separation.

Teaching formation

The course provides a solid basis of concepts before developing both theoretical and applied understanding of a range of methods in this field. In particular we consider both finite volume method and lattice Boltzmann method which are two popular tools in this field; in the latter case we also consider the possibility of interactive CFD. We move on to consider how to model the response of the structure to the aerodynamic load on it, both in terms of rigid body motion and for flexible bodies. In doing so we give examples of methods for both low & high rates of deformation.

Learning outcomes

  • Good understanding of pros and cons of popular CFD methods for fluid dynamics with special focus on aerodynamics;
  • Good understanding of methods for fluid structure interaction with examples of both low and high rates of structural deformation;
  • Good understanding of turbulence and the challenge it presents for CFD.

Pre-requirements

  • Basic fluid mechanics.
  • Basic computer skills.

Unit 2: Experimental Fluid Dynamics and Autonomous Systems

Unit summary

The learning objectives for this short course are for students to understand and be able to apply the basics of experimental design and understand the methodologies of wind tunnel testing, autonomous vehicle design and microsystems.

Teaching formation

Consideration will be given to traditional and state of the art methods with practical laboratory sessions demonstrating both components. We provide an understanding of Unmanned airborne Vehicles both in terms of how they are designed and how they are used in practice. We also provide an overview of microsystems and their application in this field.

Learning outcomes

  • Good understanding of experimental methods for aerodynamics;
  • Good understanding of design and testing of autonomous systems;
  • Good understanding of basics of microsystems.

Pre-requirements

  • Basic practical skills.
  • Basic data analysis skills.

Stream 6: Physics and Astronomy

"From the quantum to the cosmos"

The modules in this stream will present four areas of forefront physics.

The Quantum Universe will explore the often counter-intuitive behaviour of the smallest components of the cosmos.

Building on this, Exotic Materials: Graphene and Beyond, will discuss how our understanding of quantum properties of materials is being used to produce new materials with remarkable properties.

The Physics of the Nucleus module will allow you to explore the properties of the nucleus of atoms though a combination of laboratory experimentation and computer simulation as well as provide an introduction of the current state of high energy particle physics.

Using a combination of lectures and experiments in Understanding the Cosmos, you will also explore the contents of the Universe and how we use physics to understand planets, stars and galaxies as well as the Universe as a whole.

The course will include a field trip to the Jodrell Bank Observatory, home to the international headquarters of the Square Kilometre Array project, to see one of the largest radio telescopes in the world as well as a tour of the National Graphene Institute.

More details about the courses to come.