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Applied and Computational Mathematics Master of Science Degree
Rochester , United Kingdom | New York , United States
Tuition Fee
Per year
Start Date
2024-09-15
Medium of studying
On campus
Duration
Not Available
Program Facts
Program Details
Degree
Masters
Major
Computational Science | Mathematics
Discipline
Computer Science & IT | Science
Minor
Computational Mathematics | Numeracy and Computational Skills
Education type
On campus
Timing
Full time
Course Language
English
Intakes
| Program start date | Application deadline |
| 2023-09-02 | - |
| 2024-01-20 | - |
| 2024-09-15 | - |
About Program
Program Overview
An applied and computational mathematics master’s degree that is designed for you to create innovative computing solutions, mathematical models, and dynamic systems to solve problems in industries such as engineering, biology, and more.
Co-op/Internship Encouraged
STEM-OPT Visa Eligible
Program Outline
The applied and computational mathematics master’s degree refines your capabilities in applying mathematical models and methods to study a range of problems, with an emphasis on developing and implementing computing solutions. Sophisticated mathematical tools are increasingly used to solve problems in management science, engineering, biology, financial portfolio planning, facilities planning, control of dynamic systems, and design of composite materials. The goal of RIT’s master's in applied mathematics is to find computing solutions to real-world problems.
Applied and Computational Mathematics
The ideas of applied mathematics pervade several applications in a variety of businesses and industries as well as the government. The reasoning, deduction, and logic skills developed in this program will make you more competitive in a wide array of positions and industries.
Sophisticated mathematical tools are increasingly used to develop new models, modify existing ones, and analyze system performance. This includes applications of mathematics to problems in management science, biology, portfolio planning, facilities planning, control of dynamic systems, and design of composite materials. The goal of this mathematics master's degree is to find computable solutions to real-world problems arising from these types of situations.
RIT’s Master’s in Applied Mathematics
RIT’s mathematics master’s provides you with the capability to apply mathematical models and methods to study various problems that arise in industry and business, with an emphasis on developing computable solutions that can be implemented.
Tailor the applied and computational mathematics master’s degree to fit your career goals. Electives may be selected from the graduate course offerings in the School of Mathematical Sciences or from other RIT graduate programs, with approval from the graduate program director. You also have the option to complete a thesis, which includes the presentation of original ideas and solutions to a specific mathematical problem. The proposal for the thesis work and the results must be presented and defended before the advisory committee.
Applied Mathematics Careers
Graduates of the masters in applied mathematics are uniquely qualified to address the full breadth of mathematical challenges and have developed a depth of knowledge in their chosen specializations.
The Department of Defense accounts for about 81 percent of the mathematicians employed by the federal government. In the private sector, mathematicians are employed by scientific research and development services, software publishers, insurance companies, and in aerospace or pharmaceutical manufacturing.
Recent graduates are employed as engineers, economists, analysts (e.g. operations research), physicists, cryptanalysts (codes), actuaries, teachers, market researchers, and financial advisors. Apple, BAE Systems, Ernst & Young, IBM, and Microsoft are just a few of the employers who have hired graduates of the applied and computational mathematics program.
Students are also interested in: Applied Statistics MS, Applied Statistics Adv. Cert., Mathematical Modeling Ph.D.
Careers and Experiential Learning
Salary and Career Information for Applied and Computational Mathematics MS
Cooperative Education
What makes an RIT science and math education exceptional? It’s the ability to complete science and math co-ops and gain real-world experience that sets you apart. Co-ops in the College of Science include cooperative education and internship experiences in industry and health care settings, as well as research in an academic, industry, or national lab. These are not only possible at RIT, but are passionately encouraged.
What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires.
National Labs Career Events and Recruiting
The Office of Career Services and Cooperative Education offers National Labs and federally-funded Research Centers from all research areas and sponsoring agencies a variety of options to connect with and recruit students. Students connect with employer partners to gather information on their laboratories and explore co-op, internship, research, and full-time opportunities. These national labs focus on scientific discovery, clean energy development, national security, technology advancements, and more. Recruiting events include our university-wide Fall Career Fair, on-campus and virtual interviews, information sessions, 1:1 networking with lab representatives, and a National Labs Resume Book available to all labs.
Applied and Computational Mathematics (thesis option), MS degree, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| 9 | ||
| MATH-601 | Methods of Applied Mathematics |
|
This course is an introduction to classical techniques used in applied mathematics. Models arising in physics and engineering are introduced. Topics include dimensional analysis, scaling techniques, regular and singular perturbation theory, and calculus of variations. (Prerequisites: MATH-221 and MATH-231 or equivalent courses or students in the ACMTH-MS or MATHML-PHD programs.) Lecture 3 (Spring). | ||
| MATH-602 | Numerical Analysis I |
|
This course covers numerical techniques for the solution of nonlinear equations, interpolation, differentiation, integration, and matrix algebra. (Prerequisites: ((MATH-241 or MATH-241H) and MATH-431) or equivalent courses or graduate standing in ACMTH-MS or MATHML-PHD programs.) Lecture 3 (Fall). | ||
| MATH-605 | Stochastic Processes |
|
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows. (Prerequisites: ((MATH-241 or MATH-241H) and MATH-251) or equivalent courses or graduate standing in ACMTH-MS or MATHML-PHD or APPSTAT-MS programs.) Lecture 3 (Spring). | ||
| MATH-622 | Mathematical Modeling I |
|
This course will introduce graduate students to the logical methodology of mathematical modeling. They will learn how to use an application field problem as a standard for defining equations that can be used to solve that problem, how to establish a nested hierarchy of models for an application field problem in order to clarify the problem’s context and facilitate its solution. Students will also learn how mathematical theory, closed-form solutions for special cases, and computational methods should be integrated into the modeling process in order to provide insight into application fields and solutions to particular problems. Students will study principles of model verification and validation, parameter identification and parameter sensitivity and their roles in mathematical modeling. In addition, students will be introduced to particular mathematical models of various types: stochastic models, PDE models, dynamical system models, graph-theoretic models, algebraic models, and perhaps other types of models. They will use these models to exemplify the broad principles and methods that they will learn in this course, and they will use these models to build up a stock of models that they can call upon as examples of good modeling practice. (This course is restricted to students in the ACMTH-MS or MATHML-PHD programs.) Lecture 3 (Fall). | ||
| MATH-645 | Graph Theory |
|
This course introduces the fundamental concepts of graph theory. Topics to be studied include graph isomorphism, trees, network flows, connectivity in graphs, matchings, graph colorings, and planar graphs. Applications such as traffic routing and scheduling problems will be considered. (This course is restricted to students with graduate standing in the College of Science or Graduate Computing and Information Sciences.) Lecture 3 (Fall). | ||
| MATH-722 | Mathematical Modeling II |
|
This course will continue to expose students to the logical methodology of mathematical modeling. It will also provide them with numerous examples of mathematical models from various fields. (Prerequisite: MATH-622 or equivalent course.) Lecture 3 (Spring). | ||
| MATH-606 | Graduate Seminar I |
1 |
The course prepares students to engage in activities necessary for independent mathematical research and introduces students to a broad range of active interdisciplinary programs related to applied mathematics. (This course is restricted to students in the ACMTH-MS or MATHML-PHD programs.) Lecture 2 (Fall). | ||
| MATH-607 | Graduate Seminar II |
1 |
This course is a continuation of Graduate Seminar I. It prepares students to engage in activities necessary for independent mathematical research and introduces them to a broad range of active interdisciplinary programs related to applied mathematics. (Prerequisite: MATH-606 or equivalent course or students in the ACMTH-MS or MATHML-PHD programs.) Lecture 2 (Spring). | ||
MATH Graduate Electives |
9 | |
| Second Year | ||
| MATH-790 | Research & Thesis |
7 |
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course is restricted to students in the ACMTH-MS or MATHML-PHD programs.) Thesis (Fall, Spring, Summer). | ||
MATH Graduate Elective |
3 | |
| Total Semester Credit Hours | 30 |
|
Applied and Computational Mathematics (project option), MS degree, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
9 |
||
| MATH-601 | Methods of Applied Mathematics |
|
This course is an introduction to classical techniques used in applied mathematics. Models arising in physics and engineering are introduced. Topics include dimensional analysis, scaling techniques, regular and singular perturbation theory, and calculus of variations. (Prerequisites: MATH-221 and MATH-231 or equivalent courses or students in the ACMTH-MS or MATHML-PHD programs.) Lecture 3 (Spring). | ||
| MATH-602 | Numerical Analysis I |
|
This course covers numerical techniques for the solution of nonlinear equations, interpolation, differentiation, integration, and matrix algebra. (Prerequisites: ((MATH-241 or MATH-241H) and MATH-431) or equivalent courses or graduate standing in ACMTH-MS or MATHML-PHD programs.) Lecture 3 (Fall). | ||
| MATH-605 | Stochastic Processes |
|
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows. (Prerequisites: ((MATH-241 or MATH-241H) and MATH-251) or equivalent courses or graduate standing in ACMTH-MS or MATHML-PHD or APPSTAT-MS programs.) Lecture 3 (Spring). | ||
| MATH-622 | Mathematical Modeling I |
|
This course will introduce graduate students to the logical methodology of mathematical modeling. They will learn how to use an application field problem as a standard for defining equations that can be used to solve that problem, how to establish a nested hierarchy of models for an application field problem in order to clarify the problem’s context and facilitate its solution. Students will also learn how mathematical theory, closed-form solutions for special cases, and computational methods should be integrated into the modeling process in order to provide insight into application fields and solutions to particular problems. Students will study principles of model verification and validation, parameter identification and parameter sensitivity and their roles in mathematical modeling. In addition, students will be introduced to particular mathematical models of various types: stochastic models, PDE models, dynamical system models, graph-theoretic models, algebraic models, and perhaps other types of models. They will use these models to exemplify the broad principles and methods that they will learn in this course, and they will use these models to build up a stock of models that they can call upon as examples of good modeling practice. (This course is restricted to students in the ACMTH-MS or MATHML-PHD programs.) Lecture 3 (Fall). | ||
| MATH-645 | Graph Theory |
|
This course introduces the fundamental concepts of graph theory. Topics to be studied include graph isomorphism, trees, network flows, connectivity in graphs, matchings, graph colorings, and planar graphs. Applications such as traffic routing and scheduling problems will be considered. (This course is restricted to students with graduate standing in the College of Science or Graduate Computing and Information Sciences.) Lecture 3 (Fall). | ||
| MATH-722 | Mathematical Modeling II |
|
This course will continue to expose students to the logical methodology of mathematical modeling. It will also provide them with numerous examples of mathematical models from various fields. (Prerequisite: MATH-622 or equivalent course.) Lecture 3 (Spring). | ||
| MATH-606 | Graduate Seminar I |
1 |
The course prepares students to engage in activities necessary for independent mathematical research and introduces students to a broad range of active interdisciplinary programs related to applied mathematics. (This course is restricted to students in the ACMTH-MS or MATHML-PHD programs.) Lecture 2 (Fall). | ||
| MATH-607 | Graduate Seminar II |
1 |
This course is a continuation of Graduate Seminar I. It prepares students to engage in activities necessary for independent mathematical research and introduces them to a broad range of active interdisciplinary programs related to applied mathematics. (Prerequisite: MATH-606 or equivalent course or students in the ACMTH-MS or MATHML-PHD programs.) Lecture 2 (Spring). | ||
MATH Graduate Electives |
9 | |
| Second Year | ||
| MATH-790 | Research & Thesis |
4 |
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course is restricted to students in the ACMTH-MS or MATHML-PHD programs.) Thesis (Fall, Spring, Summer). | ||
MATH Graduate Electives |
6 | |
| Total Semester Credit Hours | 30 |
|
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