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MSc in Computational Mathematics

Overview

The BSc graduates who have a strong background in mathematics would like to pursue and advance their computational talents in higher-level degrees. The Department of Mathematics at KU intends to launch a two-year (four-semester) master's degree program in computational mathematics to meet the needs of the aforementioned students. With an emphasis on creating and putting into practice computing solutions, this curriculum will equip the students to hone their abilities in utilizing mathematical tools and computational skills to investigate a wide range of problems appearing in academia, industry, and technology.

  • Course Info

    Affilation Kathmandu University (KU)
    Duration 2 Years
    Institute Type College
    Average Fees Incurred NPR 420,000
    Employment Roles

    Computational Mathematician 

    Research Scientist 

    Data Scientist 

    Academician 
    Placement Opportunities

    Research Institutions 

    Universities

    IT Companies 

    Financial Institutions 

  • Admission Criteria

    Candidates may be required to qualify an entrance exam or an Interview.

  • Salient Features

    Advanced Mathematical Exploration: In-depth study of advanced mathematical concepts and theories.
    Advanced Numerical Techniques: Mastery of advanced numerical analysis and algorithm design.
    Specialized Computational Linear Algebra: Advanced applications of linear algebra in computational mathematics.
    Advanced Partial Differential Equations: In-depth study of partial differential equations and their computational solutions.
    Cutting-edge Mathematical Modeling: Advanced techniques for mathematical modeling and simulation.
    Advanced Optimization Methods: Mastery of advanced optimization theory and its applications.
    Stochastic Processes and Computations: Advanced study of stochastic processes and their computational aspects.

  • Eligibility

    One must have completed their graduation in any relevant stream with a minimum of 50% in aggregate score or CGPA of 2.0 or second division from recognized universities.

  • Curricular Structure

    Advanced Mathematical Analysis: In-depth study of advanced mathematical concepts and theories.
    Numerical Analysis and Algorithms: Advanced numerical techniques and algorithm design.
    Computational Linear Algebra: Advanced applications of linear algebra in computational mathematics.
    Partial Differential Equations: Study of partial differential equations and their computational solutions.
    Mathematical Modeling and Simulation: Advanced techniques for mathematical modeling and simulation.
    Optimization Theory: Advanced optimization methods and their applications.
    Stochastic Processes: Advanced study of stochastic processes and their computational aspects.

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