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                    Graduate opportunities in
                    Dynamical Systems


Master in Applied Mathematics with
concentration in Dynamical Systems and Chaos
MS code: 776316

The Nonlinear Dynamical Systems (NLDS) group is offering an exciting new graduate program in Applied Mathematics with emphasis in Dynamical Systems. The focus of this program is on the mathematical analysis of nonlinear phenomena and their applications. In particular, students will learn useful analytical and computational techniques to model and analyze real-life problems in all branches of applied science (cf. Physics, Engineering, Biology, Chemistry, etc...).

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Ph.D. in Computational Sciences with
concentration in Dynamical Systems and Chaos


Within the Ph.D. in Computational Sciences, the Nonlinear Dynamical Systems (NLDS) group is welcoming candidates to pursue a Ph.D. in areas of Dynamical Systems / Applied Mathematics that are compatible with the group's interests. For a sample of the group's current research interests click here.



 

Maintained by Ricardo Carretero
Ricardo Carretero Gonzalez Antonio Palacios Peter Blomgren Joe Mahaffy Diana Verzi Chris Curtis San Diego San Diego State University SDSU California West coast MS master masters PhD doctorate doctoral graduate undergraduate concentration emphasis applied mathematics chaos chaotic fractal fractals dynamics dynamical systems nonlinear nonlinear dynamical systems nonlinear dynamics NLDS model modeling modelling publication publications research preprints analysis adaptivity aggregation bifurcation bifurcations bioloby blowup blow up blow-up bose bose-einstein breather breathers CML CMLs condensates coupled map lattices delay differential determinism deterministic differential einstein embedding equation equations fluidization fluidized GPE heteroclinic homoclinic ILM ILMs image restoration intrinsic localized modes lattices manifold map maps math mathematical bioloby metastability moving mesh NLS nonlinear waves numerics numerical ODE ODEs orbit orbits pattern patterns PDE PDEs POD prediction proper orthogonal decomposition reconstruction soliton solitons spatio temporal stable stochastic studies study systems tangle temporal time series unstable