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MSc Applied Computational Science and Engineering

Imperial College London

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Course options

  • Qualification

    MSc - Master of Science

  • Location

    South Kensington Campus

  • Study mode

    Full time

  • Start date

    SEP

  • Duration

    1 year

Course summary

This course will expand your knowledge of numerical methods, computational science, and how to solve large scale problems by applying novel science and engineering approaches. It is suitable for graduates of disciplines including maths and physical sciences, geophysics and engineering, and computer science. Our MSc in Applied Computational Science and Engineering will educate future domain-specialists in computational science. This immersive, hands-on MSc course will enable students to develop their skills and techniques for a range of science and engineering applications utilising High Performance Computing resources. Students will learn alongside world-class researchers in the Department of Earth Science and Engineering. There will be a strong emphasis on high productivity problem solving using modern computational methods and technologies, including computer code development and parallel algorithms. Applicants who want to pursue analytical careers in industries across science and engineering are a target for this course. Graduates will develop the skills necessary to enter the modern industrial workforce. This MSc will also prepare for your PhD studies in fields such as computational techniques, numerical analysis, optimisation and inversion, fluid mechanics, heat transfer, and machine learning applications. The Applied Computational Science and Engineering MSc programme will ensure that students are able to apply appropriate computational techniques to understand, define and develop solutions to a range of science and engineering problems. Students will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills that employers want. Graduates of this course will fill the market demand for those with applied, hands-on computational experience who can solve real world problems. Through the combination of programming, foundational domain knowledge and advanced numerical literacy that this course provides, graduates will be highly sought after to work as expert analysts in industry, for example oil and gas, mineral exploration and climate science. Graduates will be in an ideal position to pursue academic careers in fields such as computational techniques, optimisation and inversion, fluid mechanics, and machine learning applications.

Tuition fees

Students living in United States
(international fees)

£ 32,500per year

Tuition fees shown are for indicative purposes and may vary. Please check with the institution for most up to date details.

University information

Imperial College London

  • University League Table

    5th

  • Campus address

    Imperial College London, South Kensington Campus, Kensington and Chelsea, SW7 2AZ, England

Subject rankings

  • Subject ranking

    4th out of 103 1

    21st out of 59 9

    4th out of 110 1

    4th out of 32

    6th out of 49 1

  • Entry standards

    / Max 240
    179 78%

    10th

    5
  • Graduate prospects

    / Max 100
    88 88%

    2nd

  • Student satisfaction

    / Max 5
    3.95 79%

    77th

    20
  • Entry standards

    / Max 240
    157 69%

    16th

    9
  • Graduate prospects

    / Max 100
    82 82%

    23rd

    1
  • Student satisfaction

    / Max 5
    3.64 73%

    57th

    1
  • Entry standards

    / Max 236
    206 86%

    6th

    3
  • Graduate prospects

    / Max 100
    92 92%

    15th

  • Student satisfaction

    / Max 5
    4.07 81%

    23rd

    15
  • Entry standards

    / Max 241
    183 82%

    6th

  • Graduate prospects

    / Max 100
    92 92%

    3rd

  • Student satisfaction

    / Max 5
    4.27 85%

    3rd

    2
  • Entry standards

    / Max 240
    201 87%

    6th

    1
  • Graduate prospects

    / Max 100
    88 88%

    6th

  • Student satisfaction

    / Max 5
    3.56 71%

    47th

    3

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