Rice University

What you'll study

The curriculum emphasises mathematical foundations for modelling, computational methods and applications of systems thinking. Core topics typically include dynamical systems and stability theory, network science, control and optimisation, stochastic processes and information theory. Coursework combines analytical modules with hands-on computational lab classes and a substantial culminating project or thesis that applies methods to a real-world problem.

• Dynamical systems and nonlinear analysis — phase-plane methods, bifurcation, stability and Lyapunov techniques.
• Control theory and optimisation — feedback design, optimal control, convex optimisation and numerical methods.
• Networked and complex systems — structure and dynamics of networks, diffusion, contagion and resilience.
• Stochastic modelling and inference — stochastic differential equations, Markov processes and filtering.
• Computational modelling and simulation — agent-based models, scientific computing, verification and sensitivity analysis.
• Data-driven methods — system identification, machine learning for dynamical systems, time-series analysis.

Electives allow students to tailor the programme toward applications in robotics, control systems, computational biology, energy systems, finance or social systems. Many students take interdisciplinary courses from departments such as Electrical and Computer Engineering, Computational and Applied Mathematics, BioSciences and Management.

Entry requirements

Applicants are expected to hold a bachelor’s degree (or equivalent) in engineering, mathematics, physics, computer science, or a closely related quantitative field. A solid background in undergraduate calculus, linear algebra, differential equations and basic probability/statistics is required. Demonstrated programming experience (e.g. Python, MATLAB) is highly recommended.

• Official transcripts from all post-secondary institutions attended.
• A personal statement explaining academic preparation and research or application interests in systems science.
• Letters of recommendation from academic or professional referees who can speak to quantitative skills and potential for graduate study.
• Proof of English language proficiency for international applicants where applicable.

Some applicants may benefit from prior research or industry experience in modelling, controls, data analytics or related projects. Admissions may consider GRE scores where provided, but requirements can vary; prospective students should consult the programme’s admissions pages for current guidance.

Career prospects

Graduates gain skills applicable across industry, government and academia. Typical roles include systems engineer, control systems designer, data scientist for dynamical applications, simulation and modelling engineer, and quantitative analyst. Alumni also move into specialised positions in robotics, aerospace, energy systems, healthcare analytics and computational biology.

Many graduates pursue further research at the PhD level or join interdisciplinary teams in consulting firms, technology companies, national laboratories and start-ups. The programme’s combination of theoretical rigour and practical project experience prepares students for positions that require bridging mathematical methods with implementation and decision-making.

Why study at Rice University

Rice offers a compact, research-intensive environment with strong cross-disciplinary collaboration between engineering, natural sciences and data-oriented centres. Students benefit from close faculty mentorship, access to modern computational facilities and project opportunities linked to Houston’s vibrant energy, medical and technology sectors.

The university’s emphasis on hands-on research, small class sizes and an active culture of entrepreneurship and interdisciplinary centres makes it well suited for systems science students who want both deep analytical training and opportunities to deploy methods on applied problems. Proximity to hospitals, research institutes and industry partners provides additional avenues for applied projects and internships.