Brown University’s on-campus master's in data-enabled computational engineering and science equips you with technical skills and deep understanding needed for real-world applications in national laboratories and industries.
As a world leader in applied mathematics, solid mechanics, materials science and fluid and thermal systems, Brown’s School of Engineering is uniquely positioned to offer the master’s in data-enabled computational engineering and science program.
This program will prepare you to excel in pursuing a Ph.D. or a career at the forefront of advanced modeling and simulation in engineering and physical sciences. Whether you're a recent graduate, rising professional or an experienced engineer, you'll master high-fidelity simulations, data assimilation and machine learning techniques essential for solving complex real-world problems.
This program offers the following degree with three track options:
- Master of science (Sc.M.)
- Thesis (Coursework and thesis)
- Non-Thesis (Coursework only)
- Professional (Coursework and internship)
Through the highly-interdisciplinary curriculum, you’ll gain a deep understanding of the significant role that advanced simulation plays in industry and national laboratories, while acquiring technical skills in computational engineering and machine learning expertise. This includes mastering nonlinear finite element analysis and the integration of physics-based modeling with data science. You will become comfortable using all the established software programs (e.g., MATLAB, Python) and new software (e.g., TensorFlow).
During the program, you’ll work with accomplished faculty who are developing state-of-the-art numerical methods and machine learning approaches that are directly applicable to this program. The small cohort allows you to be advised by the leading faculty, develop strong long term relationships with your peers, receive Ph.D. and career application coaching, and obtain access to the Brown alumni network.
Additional Information
Brown undergraduates can apply to this program as a fifth-year master’s degree.
Application Information
The master of science degree program in Data-Enabled Computational Engineering and Science is designed for students who have recently completed their bachelor’s degree (<5 years). Current Brown University seniors are encouraged to apply for a fifth-year master's degree.
If you have any questions regarding the application process for this program, please email masters_admissions@brown.edu.
Application Requirements
GRE General:
Required; the GRE General Test at home version is accepted (not required for Brown fifth-year master’s applicants)
TOEFL/IELTS:
Required for any non-native English speaker who does not have a degree from an institution where English is the sole language of instruction or from a University in the following countries: Australia, Bahamas, Botswana, Cameroon, Canada (except Quebec), Ethiopia, Ghana, Ireland, Kenya, Lesotho, Liberia, Malawi, New Zealand, Nigeria, Zimbabwe, South Africa, Sierra Leone, Swaziland, Tanzania, Gambia, Uganda, United Kingdom (England, Scotland, Northern Ireland, Wales), West Indies, Zambia).
The TOEFL iBT Special Home Edition and the IELTS Indicator exam are accepted. Students from mainland China may submit the TOEFL ITP Plus exam.
Official Transcripts:
Required. All applicants may upload unofficial transcripts for application submission. Official transcripts are ONLY required for enrolling students before class start. An international transcript evaluation (WES, ECE, or SpanTran) is required for degrees from non-U.S. institutions before enrollment.
Letters of Recommendations:
Three letters of recommendation
Additional Materials:
Bachelor’s degree in Engineering, Physical Sciences, Natural Sciences, or related subject
The strongest preference is given to those with an undergraduate GPA of at least 3.4
Dates/Deadlines
Priority Deadline
Application Deadline
5th Year Deadline
Tuition and Funding
Graduate Tuition & Fees: Please visit the Student Financial Services Office for up-to-date tuition rates.
Completion Requirements
Students will take a total of eight courses to satisfy the degree requirements. To ensure breadth, students are expected to take at least two courses in an engineering or natural science focus area of choice (e.g., mechanics of solids, materials science, etc.), at least two courses in applied mathematics and at least two courses in data science/high performance computing. The remaining two courses may be taken in engineering, applied mathematics, data science or other relevant disciplines.
Students should choose courses in consultation with their advisor to develop a coherent program. The proposed program of study must be approved by the academic director of data-enabled computational engineering and science master’s program in the School of Engineering.
Thesis
Students choose this option if they are interested in pursuing a Ph.D. They must complete a coherent plan of study based in data-enabled computational engineering and science consisting of eight graduate or advanced level courses and an acceptable thesis, which is normally sponsored by a member of the Engineering or APMA faculty.
Students are expected to complete the thesis option in three or four semesters. In the three-semester format, the students are expected to take three courses in the first semester, three in the second semester and two in the third semester. However, in the second and third semesters, the students may sign up for a “Special Topics: Reading Research and Design” type class (i.e., ENGN 2980 or equivalent in APMA) to satisfy the eight-course requirement.
In the four-semester model, students are expected to take three courses in the first semester, two in the second semester, two in the third semester and one in the fourth semester. In both cases, the “Special Topics: Reading Research and Design” course may be counted up to two times toward the degree.
Non-Thesis
Students choose this option if they want a degree to help achieve their personal or professional goals. They must complete a coherent plan of study based in data-enabled computational engineering and science consisting of eight graduate or advanced level courses.
Students are expected to complete the non-thesis option in three semesters, taking three courses in the first and second semesters, and two courses in the third semester (i.e., the 3-3-2 model).
One-year completion is also possible with students taking four courses in the first semester and four courses in the second (i.e., the 4-4 model). The maximum duration students may take to complete will be four semesters.
Professional
Students choose this option if they are focused on their professional goal of obtaining a position that applies data-enabled computational engineering and science in national laboratories or industries. They must complete a coherent plan of study based in data-enabled computational engineering and science consisting of eight graduate or advanced level courses. In addition to the course requirements, a paid or unpaid internship is a required component. Assistance in obtaining internships will be provided by the School of Engineering and the Center for Master’s Student Excellence.
Students are expected to complete the professional option in three semesters, taking three courses in the first and second semesters, and two courses in the third semester (i.e., the 3-3-2 model). The maximum duration students may take to complete will be four semesters (i.e., the 2-2-2-2 model).
A paid or unpaid experiential learning experience of three to six months is a required component. Experiential learning can include one of the following (but not both):
- A summer internship directly related to the program of study
- Completion of ENGN 2960 (Experiential Learning in Industry (ELI)) as an elective course that counts towards the eight-course requirement. Note: students enrolled in ENGN 2960 are considered full-time students and may be counted only one time toward the degree.