Applied Mathematics and Matematical Statistics Graduate School.pdf

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