UMD’s MS in Applied Mathematics and Scientific Computation equips students to solve complex problems in engineering, data science, physics, and finance. Students explore numerical methods, PDEs, optimization, and machine learning. The program emphasizes research and computational modeling across scientific and industrial applications, preparing graduates for tech and R&D roles or PhD programs.
Numerical simulation of turbulent fluid flow using finite volume methods
Mathematical modeling of epidemic spread under vaccination strategies
Machine learning for predictive modeling in financial markets
Solving PDEs related to groundwater contamination using multigrid methods
Optimization algorithms for resource allocation in logistics networks
Development of a climate model for temperature anomaly forecasting
Stochastic modeling of population dynamics in predator-prey systems
Computational analysis of structural deformation under load
Simulating cardiac electrophysiology using parallel computing
Mathematical model of neuronal signal propagation using differential equations
Deep learning-based solution approximator for high-dimensional PDEs
Statistical modeling of genetic expression data in cancer research
Development of a finite element library for solid mechanics applications
Simulation of airflow and drag reduction in aerodynamic systems
Data assimilation techniques in real-time flood forecasting models
Analysis of option pricing models using Monte Carlo methods
Inverse problem formulation in geophysical imaging
Graph theory application to optimize urban traffic networks
Simulating elastic wave propagation in seismology using spectral methods
Creating a software package for dynamic system modeling and control
Use mathematical theory and computation to solve problems in climate modeling, finance, and physics through UMD’s applied mathematics program.
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