Program MRes Machine Learning and Big Data in the Physical Sciences Physics Imperial College London

MRes Machine Learning and Big Data in the Physical Sciences

Inggris

18

Apa yang akan saya pelajari?

Further your understanding of the methodologies and toolkits used in research involving large data sets on this Master's course. This course is excellent preparation for a PhD place, or for a role in the expanding data science industry.Explore how the field of physics provides a unique development ground for machine learning and artificial intelligence.You'll examine the use of machine learning and data-science techniques in the acquisition, curation and analysis of large datasets commonplace in modern physics research.You'll also explore how different techniques can be deployed in real research and how to apply these tools to real-life experimental data.An extended full-time project forms the major component of this course, providing you with an opportunity to join a research team and carry out world-class research in a cutting-edge physics topic.Learn alongside world-leading experts at Imperial and deploy the latest data science technologies to enhance your research.

Saya termasuk departemen apa?

Physics

Pilihan kuliah

Purna Waktu (1 tahun)

Biaya kuliah
£44.500,00 (Rp 900.534.015) per tahun
Ini adalah biaya tetap
Batas akhir pendaftaran

Direncanakan August 2027

Tanggal mulai

September 2026

Tempat

White City Campus

80–92 Wood Lane,

London,

Greater London,

W12 0BZ, Southern England, United Kingdom

Purna Waktu (1 tahun)

Biaya kuliah
£44.500,00 (Rp 900.534.015) per tahun
Ini adalah biaya tetap
Batas akhir pendaftaran

Direncanakan August 2027

Tanggal mulai

September 2026

Tempat

Imperial College London - South Kensington Campus

Exhibition Road,

London,

Greater London,

SW7 2AZ, Southern England, United Kingdom

Persyaratan masuk

Untuk mahasiswa internasional

2:1 degree or three years of relevant work experience in the physical sciences (such as physics, chemistry, applied mathematics) or appropriate quantitative disciplines such as engineering, finance, medical clinical, or transportation.

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