Applied Mathematics and Matematical Statistics Graduate School.pdf - Applied mathematics and mathematical statistics The graduate school is organised

Applied Mathematics and Matematical Statistics Graduate School.pdf

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Applied mathematics and mathematical statistics The graduate school is organised within the Department of Mathematical Sciences. . Deputy head of department: Aila Särkkä Director of Graduate Studies: Marija Cvijovic Syllabus (approved by the vice-rector for research education on October 12, 2015. Reference number C 2015-1445) 1. Description of subject and goals The purpose of the graduate school in applied mathematics and mathematical statistics is to give the student fundamental knowledge within applied mathematics, orientation about current problems and applications, a deeper insight into one or several parts of the subject, and the ability to independently carry out research work. The aim of the program until the licentiate degree is to give the student the ability to independently take part in research and development work. The aim of the program until the doctoral degree is to give the student the ability to critically and independently plan, lead, carry through, and present research and development work. The studies within the school are typically performed in connection to research topics that are actively pursued at the faculty. Example of such topics are: Computational mathematics Computational mathematics is a field that studies problems both in pure and applied mathematics, using methods based on a synthesis of mathematical analysis and numerical/symbolical computation. Computational mathematics treats the whole process from mathematical model to computer implementation. Development and analysis of computational algorithms are crucial components. Questions considered include stability, convergence, and efficiency of computational methods. An important area in computational mathematics is the modelling and numeric of partial differential equations. This area includes the construction and analysis of efficient computational algorithms in numerical linear algebra, methods of discretization such as the finite element method, as well as different aspects of high performance computing such as adaptivity and effective use of parallel computer architectures. Common applications in computational mathematics are structural mechanics, fluid mechanics, biomedicine, architecture, and mathematical physics. Modelling of kinetics and dynamical system Kinetic and dynamical models are tools for studying systems whose state change over time. A prime example of this is the Boltzmann equation that describes the position and velocity of molecules in a gas. Research on this topic consists both of formulating models
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  • Spring '12
  • margarettatker
  • Math, Statistics, Applied Mathematics, Mathematical statistics, Academic degree, Doctor of Philosophy, Doctorate, Postgraduate education

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