University of York

What you'll study

This integrated master’s programme builds core chemical knowledge across all years while embedding digital and computational methods throughout. Early years emphasise foundational topics: atomic and molecular structure, organic and inorganic chemistry, physical chemistry, analytical techniques and practical laboratory skills. Practical laboratory training develops experimental competence, safety awareness and data handling.

• Core chemistry modules covering organic synthesis and mechanism, inorganic structure and bonding, physical and theoretical chemistry, spectroscopy and analytical methods.
• Laboratory and practical training including multi-week practical blocks, advanced instrumental analysis (NMR, mass spectrometry, chromatography) and experimental project work.
• Digital methods modules that introduce programming for chemists (commonly Python), computational chemistry and molecular modelling, chemoinformatics, data science and statistical analysis applied to chemical data.
• Advanced and optional topics such as quantum chemistry, materials modelling, machine learning for molecular design, cheminformatics workflows, and simulation of spectroscopic properties.
• Research and capstone projects in later years include substantial extended individual research projects that can be computational, experimental or hybrid, allowing students to apply digital methods to authentic chemical research questions.

The course structure integrates progressively more independent and research-led work, culminating in an extended masters-level research project. Teaching methods combine lectures, small-group tutorials, hands-on labs, computing workshops and supervised research.

Entry requirements

Applicants should have a strong background in chemistry and mathematics from their secondary education. Typical applicants hold upper-secondary qualifications with high grades in Chemistry and at least one other science or Mathematics. International qualifications such as the International Baccalaureate, relevant national high-school diplomas or equivalent vocational qualifications are accepted where they meet the department's academic standards.

• Applicants without formal computing experience are still eligible; the course provides introductory programming and computational modules.
• Admissions consider predicted/achieved academic performance, teacher references and any relevant practical or research experience. Mature students and those with relevant work experience are welcome.
• English language proficiency must meet the University’s requirements for degree-level study if English is not your first language.

Career prospects

Graduates leave with both experimental chemistry skills and practical experience in computational and data-driven methods, making them attractive to a wide range of employers. Common career paths include roles in the pharmaceutical and chemical industries, materials and polymer companies, environmental and analytical laboratories, and energy sectors.

• Computational chemist, molecular modeller or cheminformatics specialist in industry and research organisations.
• Data scientist or analyst working on chemical and materials datasets, process optimisation or machine-learning model development.
• Analytical chemist, R&D scientist or formulation scientist in industry.
• Technical roles in instrumentation, software companies providing scientific tools, or scientific consultancy and patent work.
• Progression into PhD research and academic careers, particularly in areas combining computation and experiment.

Why study at University of York

The University of York offers a research-led chemistry department with strong emphasis on interdisciplinary collaboration between chemistry, computational sciences and engineering. Students benefit from well-equipped laboratories, dedicated computing facilities and access to high-performance computing resources for molecular modelling and data analysis.

• Teaching combines traditional chemical training with bespoke modules in digital and computational skills, designed to meet contemporary employer needs.
• Opportunities for hands-on research projects with academic supervisors across a range of experimental and computational groups.
• Links with industry and placement opportunities help students gain practical experience and prepare for professional careers.
• Supportive student community, transferable-skills training and careers services to help with internships, graduate schemes and further study applications.