University of Maine

What you'll study

The programme combines foundational coursework in mathematics and computer science with applied scientific modules and project-based learning. Early years focus on calculus, linear algebra, differential equations and introductory programming. Intermediate and advanced courses introduce algorithms, data structures, numerical analysis, scientific computing, and statistics, together with domain electives that may include physics, biology, environmental science or engineering.

Typical course topics and learning experiences include:

• Mathematics: multivariable calculus, linear algebra, ordinary differential equations and mathematical modelling.
• Computer science: programming (Python, C/C++ or similar), data structures, algorithms and software engineering principles.
• Scientific and numerical computing: numerical methods for linear and nonlinear systems, discretisation techniques, simulation and error analysis.
• Data analysis and visualization: statistical methods, machine learning basics, data wrangling and visualisation for large datasets.
• Domain applications: computational modelling in areas such as environmental modelling, computational physics, bioinformatics or engineering analysis.
• Capstone and project work: team-based projects and an independent or supervised capstone that apply computational approaches to a real-world scientific problem, often using high-performance computing resources.

Entry requirements

Applicants should have a solid background in mathematics and science from secondary education. Typical expectations include successful completion of college-preparatory courses that feature algebra, pre-calculus or calculus, and at least one laboratory science (physics, chemistry or biology).

• Academic preparation: strong grades in mathematics and science subjects; prior coursework in calculus is advantageous.
• Programming experience: not always mandatory, but familiarity with basic programming or problem-solving using computers is beneficial and recommended.
• Admissions materials: submission of secondary school transcripts is required; standardised test submission may follow the university’s current admissions policy. Students may also be asked for a personal statement outlining interest in computational science and relevant experiences.
• Transfer applicants: transfer credit is considered from accredited institutions; applicants should provide college transcripts and course descriptions for evaluation.

Career prospects

Graduates leave prepared for roles that require quantitative analysis, modelling and software skills. Career paths span industry, government and academia, and graduates commonly find positions in data science, software development, computational modelling and technical consulting.

• Data scientist / data analyst
• Computational modeller or simulation engineer (environmental, engineering, physical sciences)
• Scientific programmer or software developer working on research codes or engineering tools
• High-performance computing (HPC) specialist and systems analyst
• Quantitative analyst in finance or insurance
• Research assistant or technician in university, national laboratory or industry research groups

Why study at University of Maine

The University of Maine offers a programme grounded in interdisciplinary collaboration with access to campus research centres and computing resources. Students benefit from faculty engaged in applied research across areas such as environmental science, materials and engineering, and climate studies, enabling computational science projects tied to regional and global problems.

On-campus resources frequently available to students include high-performance computing infrastructure, specialised laboratories and opportunities to work with centres such as the Climate Change Institute and the Advanced Structures and Composites Center. The programme emphasises experiential learning through undergraduate research, internships with regional partners and a practicum or capstone project that builds a portfolio of applied computational work.