Master of Engineering (MEng)

MEng Materials Science and Engineering with Corrosion

Study materials science with a specialisation in the corrosion and protection of materials at Manchester- a world-leading centre of excellence.

  • Duration: 4 years
  • Year of entry: 2025
  • UCAS course code: F203 / Institution code: M20
  • Key features:
  • Scholarships available
  • Accredited course

Full entry requirementsHow to apply

Fees and funding

Fees

Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £38,000 per annum. For general information please see the undergraduate finance pages.

Policy on additional costs

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

Scholarships/sponsorships

The University of Manchester is committed to attracting and supporting the very best students. We have a focus on nurturing talent and ability and we want to make sure that you have the opportunity to study here, regardless of your financial circumstances.

For information about scholarships and bursaries please see our undergraduate fees pages and check the Department's funding pages .

Course unit details:
Modelling and Data Tools for Materials Scientists

Course unit fact file
Unit code MATS31101
Credit rating 10
Unit level Level 6
Teaching period(s) Semester 1
Available as a free choice unit? No

Overview

This unit introduces key techniques for the manipulation and processing of data from experiments and simulations and a range of approaches to simulating the properties and behaviour of materials. It presents the theory underlying these techniques, explains when and how they can be used and provides an opportunity for hands-on experience in using them. The unit will be delivered via computer laboratory workshops using the software Python.

Aims

This unit aims to:

  1. Introduce a range of data processing and analysis tools relevant to materials scientists and develop an understanding of how they work and a practical ability to use them through hands-on exercises
  2. Explain the concept of materials simulation, the different techniques available, the theory underlying those techniques and the types of problems they can be used to solve
  3. Provide hands-on experience of using modelling techniques to explore the behaviour of materials    

 

Learning outcomes

A greater depth of the learning outcomes will be covered in the following sections:

  • Knowledge and understanding
  • Intellectual skills
  • Practical skills
  • Transferable skills and personal qualities

Teaching and learning methods

This unit will be primarily delivered via enquiry-based learning in a computer laboratory. Hands-on learning will be reinforced by assessed computational course work. Supplementary lectures will be given to explain important theoretical concepts prior to the computer workshops. Recommended textbooks, web resources and electronic supporting information (on Blackboard) will also be provided.

 

Intellectual skills

Upon completing this unit, you should be able to:

a)      Fit datasets with appropriate mathematical functions and quantitatively assess the quality of the fits.

b)      Characterise data using statistics and construct robust statements to communicate statistical findings

c)      Use simulation techniques to predict the response of materials under given physical conditions

d)      Draw clear conclusions from the outcome of simulations, appropriate to the degree of approximation inherent in the model and the simulation design

Transferable skills and personal qualities

Upon completing this unit, you should be able to:

a)     Translate real-world, physical problems into a form that can be solved with a computer

Assessment methods

Method Weight
Written assignment (inc essay) 100%

Feedback methods

Written and verbal

Recommended reading

There is no compulsory reading material for the course. You may find the following textbook useful:

Scopatz and Huff, 2015. Effective Computation in Physics. O’Reilly Media.

 

Study hours

Scheduled activity hours
Lectures 20
Independent study hours
Independent study 80

Teaching staff

Staff member Role
Thomas Flint Unit coordinator

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