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MSc Geo-Energy with Machine Learning and Data Science

Imperial College London

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Imperial College London

Course options

  • Qualification

    MSc - Master of Science

  • Location

    South Kensington Campus

  • Study mode

    Full time

  • Start date

    SEP

  • Duration

    1 Year

Course summary

Course overview:

Discover how data science and machine learning is revolutionising the study of subsurface geoscience and engineering on this Master's course. Firstly, you'll cover the fundamental processes and applications of subsurface geoscience and engineering. Then, you'll examine how data science, numerical methods and machine learning can help solve problems in the sector. You'll have the opportunity to apply these concepts to real-world settings on a geological field trip and through an independent project in an area of your choice. You'll also advance your programming skills in Python to solve numerical problems related to subsurface processes, and design numerical models relevant to energy technologies. The course is designed to meet the needs of mathematicians or scientists keen to apply their skills to this field. Alternatively, it's also suitable if you're from a geoscience or engineering background and looking to advance your knowledge of this specialist subject area. This course is part of the Ada Lovelace Academy, an initiative from the Department of Earth Science and Engineering aiming to deliver gender-balanced post-graduate education in computational subjects to solve the science and engineering challenges of the 21st century.

Application deadline

01/09/2025

Tuition fees

Students living in United States
(International fees)

£ 43,800per 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

Imperial College London

  • University League Table

    6th

  • Campus address

    South Kensington Campus, Exhibition Rd, London, Greater London, SW7 2AZ, United Kingdom

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Subject rankings

  • Subject ranking

    Computer Science

    3rd out of 117

  • Entry standards

    / Max 223
    217 97%

    5th

  • Graduate prospects

    / Max 100
    97.0 97%

    4th

    1

  • Student satisfaction

    / Max 4
    3.13 78%

    29th

    13

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