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The MPhil in Data Intensive Science at the University of Cambridge is a highly selective, interdisciplinary, and research-driven master's program designed for students with strong quantitative backgrounds in physics, applied mathematics, engineering, or computer science. Offered by the School of Physical Sciences, the program equips students with cutting-edge expertise in data analytics, machine learning, and high-performance computing, applied to solving complex problems in scientific research.
This 10-month full-time course emphasizes open and reproducible research, enabling students to work across domains such as astrophysics, particle physics, biomedical sciences, and AI. It is an ideal stepping stone to doctoral research or data-intensive careers in academia, industry, or government labs.
Program Format & Duration
Full-time, on-campus (Cambridge, UK)
Duration: 10 months (October–July)
Award: MPhil in Data Intensive Science
Department: School of Physical Sciences (hosted by the Department of Physics)
STEM-designated
Core Curriculum Components
The curriculum is designed to train students in core data science techniques while allowing them to apply those tools in a scientific research context.
Core Modules
Foundational Training
Statistical Data Analysis
Machine Learning & AI
High-Performance Computing
Research Software Engineering
Advanced Scientific Applications (select two)
Computational Astrophysics
Particle Physics Data Analysis
Computational Biology
AI for Physical Sciences
Advanced Topics in Applied Mathematics
Programming & Tools
Python, C++, R
Git/GitHub, Linux, Docker
HPC platforms and cloud infrastructure (e.g., SLURM, AWS)
Data Analysis Research Project
Individual research project spanning two terms
Focused on validating or reproducing results from published scientific research
Includes codebase, report, executive summary, and oral presentation
Supervised by a Cambridge faculty member
The program offers immersive research experiences and exposure to collaborative, cross-disciplinary work.
Research Project
Central to the program, involving substantial, hands-on data analysis
Students apply computational methods to tackle real scientific questions
Often replicates or critiques published findings to emphasize transparency and reproducibility
Examples may include LHC data, climate models, genomic datasets, or space telescope archives
Workshops & Seminars
Core skill-building workshops in research computing and scientific communication
Seminars from Cambridge faculty and industry leaders on data-intensive challenges across fields
Training in ethical data use, FAIR data principles, and collaborative research practices
Graduates of this program are exceptionally well-prepared for PhD programs and high-level data science roles in scientific and technical domains.
Career Outcomes
Common career paths include:
Computational Scientist (Physics, Biology, Astronomy)
Data Scientist / Machine Learning Engineer
Scientific Software Developer
AI Researcher
Quantitative Analyst (Science & Tech sectors)
Top Employers & Institutions
Graduates pursue further study or employment with:
University of Cambridge (PhD, Postdocs)
CERN, Alan Turing Institute, UKRI
DeepMind, IBM Research, Microsoft Research
National labs and research-intensive startups
Further Study
Many graduates go on to:
PhDs in Data Science, Astrophysics, Computational Biology, or Applied AI
DPhil or MRes programs at Oxford, Imperial, or ETH Zurich
Research-intensive fellowships or cross-disciplinary doctoral training programs
Degree Requirement: Hold a UK Good Honours (2:1) degree or international equivalent in a science or technology discipline.Quantitative Background: Demonstrate competence in mathematics, particularly linear algebra, statistics, and probability.References: Provide two academic referee reports. Transcript: Upload a full academic transcript (official version required upon admission).CV/Resume: Submit a current CV outlining academic, technical, and research experience.Statement of Interest: Write a concise statement (1,500 characters) detailing your motivation and preferred minor modules. Statement of Interest (1500 characters, mandatory) Briefly explain your motivation for applying, your relevant experience and interests, and optionally mention any preferred minor topic areas (non-binding). This helps route your application to the most suitable academic reviewer.Career Goals (1500 characters, mandatory) Outline the kind of research you hope to pursue in the future.Research Experience (2500 characters, optional) Describe any past or current research projects you've undertaken.Sample of Work (optional) Upload a writing sample such as a course report, academic article, or any piece showcasing your suitability. For coding or data projects, please include public links (e.g., GitHub) instead of uploading source code.Deadline: Apply by 16 May 2025 for Michaelmas (October) entry; applications open on 4 September.English Language Competency: Meet minimum language scores: IELTS 7.0 (no subscore)Application Fee & Portal Submission: Complete the application via Cambridge’s portal and pay the applicable fee. Data ScientistMachine Learning EngineerData AnalystBusiness Intelligence AnalystQuantitative ResearcherData EngineerAI SpecialistResearch ScientistAnalytics Consultant and Big Data Architect
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