Columbia University

What you'll study

The programme combines advanced coursework, laboratory or computational training, and a substantial research or capstone project. Core topics typically include advanced classical and quantum mechanics, statistical and thermal physics, electrodynamics, physical chemistry, and mathematical methods for physicists. Students can also expect modules in computational physics or chemistry, numerical methods, data analysis, and specialised electives such as condensed matter physics, atomic and molecular physics, spectroscopy, materials science, or biophysics.

Teaching is delivered through lectures, supervised laboratory or computational practicals, and departmental seminars. Most students choose a research adviser and undertake a research project or master's thesis that applies theoretical, experimental or computational methods to a specific problem. Interdisciplinary options and elective courses from allied departments (e.g. applied physics, engineering, earth sciences, computer science) allow customisation of the programme to match research interests and career goals.

Entry requirements

Applicants are normally expected to hold a strong bachelor's degree in physics, chemistry, mathematics, engineering or a closely related discipline. Typical preparation includes undergraduate coursework in classical mechanics, electromagnetism, quantum mechanics, thermodynamics/statistical mechanics, and calculus-based mathematics.

• Academic transcripts showing a competitive GPA from an accredited institution.
• A written statement of purpose describing academic background, research interests and career objectives.
• Letters of recommendation from academic or professional referees who can attest to preparedness for graduate study.
• A curriculum vitae or résumé outlining relevant research, laboratory, programming or work experience.
• Proof of English language proficiency if the applicant’s first language is not English (standard tests or other accepted evidence).

Standardised test requirements (such as the GRE) vary by department and may not be required—applicants should consult the department's admissions page for current policy. Successful applicants often demonstrate research experience, programming skills (e.g. Python, MATLAB), and quantitative problem-solving ability.

Career prospects

Graduates pursue a wide range of pathways. Many continue to doctoral programmes in physics, chemistry, materials science or related fields. Others move into research roles in national laboratories, industrial research and development, materials and semiconductor companies, pharmaceutical and chemical industries, or energy and environmental firms.

The strong quantitative and computational training also prepares graduates for roles outside traditional research, including data science, quantitative finance, software engineering for scientific applications, patent and technical consulting, and technology-driven start-ups. Teaching at secondary or tertiary levels and science policy positions are further options for those combining technical expertise with communication skills.

Why study at Columbia University

Columbia offers access to a broad research ecosystem across departments and affiliated research centres, with opportunities to work alongside internationally recognised faculty in experimental and theoretical groups. Being located in New York City provides proximity to national laboratories, industry partners, start-ups and a diverse professional network for internships and collaboration.

Students benefit from extensive core facilities and instrumentation, interdisciplinary seminar series, and course options across neighbouring schools, enabling tailored training in both fundamental science and applied, computational approaches. Programme participants also gain access to career services, alumni networks and workshops that support transition into research careers or industry roles.