Example study paths

In the Master’s Programme in Theoretical and Computational Methods, you have flexibility in how you combine courses to make a cohesive programme of study. Once you start you will be appointed an academic mentor who will help you make your final course choices.

The primary study goal in the Master’s Programme in Theoretical and Computational Methods is to develop analytical and computational skills universally applicable to model various phenomena quantitatively. Below are examples of different possible study paths, each focusing on different concrete phenomena. Each example also has an outline suggesting a two-year schedule for relevant studies. These are mainly intended to provide ideas for drawing a study plan, which you can of course develop towards directions you find interesting. Your final study plan also depends on what you have already learned in your prior studies.

Please note that there might be changes in course offerings, and the actual teaching times may differ from what is presented here. Always check the availability of the courses for the current academic year in the course catalogue.

Thanks to the contracts for cross-institutional studies, you can also take certain courses at other universities in Finland, for example, at Aalto University. Browse the cross-institutional study opportunities in Studies service.
Example study paths

Compiled by Vivek Sharma

The thesis (30) would be completed in year 2, and you could attend relevant research seminars throughout the programme as well as a relevant masters seminar course (5).

For a given period, it is recommended to choose around 3 courses relevant for studies/track.

Starting in even years

Year 1, periods 1 and 2

  • Introduction to biological physics MATR331 (5)
  • Molecular electronic structure KEM369 (5)
  • Introduction to structural biology and biophysics GMB-105 (5)
  • QM and QM/MM modeling of bio/chemical systems TCM324 (5)
  • Introduction to data science DATA11001 (5)

Year 1, periods 3 and 4

  • Molecular modeling KEM342 (5)
  • Physics of biological systems MATR332 (5)
  • Basics of Monte Carlo simulations MATR323 (5)
  • Numerical methods in scientific computing MATR322 (10)
  • Programming projects in molecular modelling KEM381 (5)

Year 2, periods 1 and 2

  • Modeling of biological systems MATR333 (5)
  • Nanophysics and Nanochemistry MATR305 (5)
  • Molecular dynamics simulations MATR325 (5)
  • Experimental methods in molecular science 1 KEM346 (5)
  • Quantum chemistry and spectroscopy KEM347 (5)
  • Molecular symmetry and group theory KEM378 (5)

Year 2, periods 3 and 4

  • Tools for high performance computing MATR326 (5)
  • Methods in Molecular Biophysics MATR382 (5-10)
  • Molecular properties KEM345 (5)

Starting in odd years

Year 1, periods 1 and 2

  • Introduction to biological physics MATR331 (5)
  • Experimental methods in molecular science 1 KEM346 (5)
  • Statistical Methods PAP334 (5)
  • Modeling of biological systems MATR333 (5)
  • Introduction to machine learning DATA11002 (5)
  • Introduction to structural biology and biophysics GMB-105 (5)

Year 1, periods 3 and 4

  • Physics of biological systems MATR332 (5)
  • Basics of Monte Carlo simulations MATR323 (5)
  • Molecular modelling KEM342 (5)
  • Tools for high performance computing MATR326 (5)
  • Monte Carlo Simulations in Physics MATR324 (5)
  • Statistical Physics BSPH2012 (5)

Year 2, periods 1 and 2

  • Introduction to data science DATA11001 (5)
  • Electronic excited state dynamics of molecules KEM384 (5)
  • QM and QM/MM modeling of bio/chemical systems TCM324 (5)

Year 2, periods 3 and 4

  • Methods in Molecular Biophysics MATR382 (5-10)
  • Programming projects in molecular modelling KEM381 (5)
  • Numerical methods in scientific computing I MATR3221 (5)
  • Numerical methods in scientific computing II MATR3222 (5)

Compiled by Lucile Turc

The thesis (30) and seminar (5) would be completed in year 2.

Starting in even years

Year 1, periods 1 and 2

  • Plasma physics PAP304 (5)
  • Space applications of plasma physics PAP305 (5)
  • Space and astrophysical plasma turbulence PAP354 (5)
  • Scientific computing II FYS2085 (5)
  • Introduction to data science DATA11001 (5)
  • Data analysis with Python BSCS2015 (5)

Year 1, periods 3 and 4

  • Numerical space physics PAP324 (5)
  • Solar physics PAP321 (5)
  • Numerical methods in scientific computing MATR322 (10)
  • Basics of Monte Carlo simulations MATR323 (5)

Year 2, periods 1 and 2

  • Kinetic theory I TCM325 (5)
  • Kinetic theory II TCM326 (5)
  • Introduction to machine learning DATA11002 (5)

Year 2, periods 3 and 4

  • Advanced space plasma physics PAP323 (10)
  • Advanced course in machine learning DATA12001 (5)

Starting in odd years

Year 1, periods 1 and 2

  • Plasma physics PAP304 (5)
  • Kinetic theory I TCM325 (5)
  • Space applications of plasma physics PAP305 (5)
  • Introduction to machine learning DATA11002 (5)
  • Data Analysis with Python BSCS2015 (5)

Year 1, periods 3 and 4

  • Advanced space plasma physics PAP323 (10)
  • Basics of Monte Carlo simulations MATR323 (5)
  • Tools of high-performance computing MATR326 (5)
  • Advanced course in machine learning DATA12001 (5)

Year 2, periods 1 and 2

  • Introduction to data science DATA11001 (5)
  • Statistical Methods PAP334 (5)
  • Space and astrophysical plasma turbulence PAP354 (5)
  • Scientific computing II FYS2085 (5)

Year 2, periods 3 and 4

  • Solar physics PAP321 (5)
  • Numerical space physics PAP324 (5)
  • Numerical methods in scientific computing I MATR3221 (5)

Compiled by Kai Puolamäki

The thesis (30) and seminar (5) would be completed in year 2.

Starting in even years

Year 1, periods 1 and 2

  • Introduction to data science DATA11001 (5)
  • Design and analysis of algorithms CSM12101(5)
  • Probability theory I MAST31701 (5)
  • Introduction to machine learning DATA11002 (5)
  • Bayesian data analysis LSI35002 (5)
  • Engineering of machine learning systems DATA11008 (5)

Year 1, periods 3 and 4

  • Data science project I DATA11004 (5)
  • Approximation algorithms CSM12106 (5)
  • Algorithms in genome analysis LSI31007 (5)
  • Advanced Bayesian inference MAST32004 (5)
  • Advanced course in machine learning DATA12001
  • Neural networks and deep learning DATA20046 (5)

Year 2, periods 1 and 2

  • Inverse problems 1: convolution and deconvolution MAST31401 (5)
  • Big data platforms DATA14003 (5)
  • Probability theory II MAST31702 (5)
  • Combinatorial optimisation CSM12107 (5)
  • Introduction to real and Fourier analysis MAST30132 (5)

Year 2, periods 3 and 4

The final periods are dedicated to writing the thesis.

Starting in odd years

Year 1, periods 1 and 2

  • Introduction to data science DATA11001 (5)
  • Design and analysis of algorithms CSM12101 (5)
  • Probability theory I MAST31701 (5)
  • Introduction to machine learning DATA11002 (5)
  • Engineering of machine learning systems DATA11008 (5)
  • Bayesian data analysis LSI35002 (5)

Year 1, periods 3 and 4

  • Data science project I DATA11004 (5)
  • Advanced course in machine learning DATA12001 (5)
  • Neural networks and deep learning DATA20046 (5)
  • Advanced Bayesian inference MAST32004 (5)

Year 2, periods 1 and 2

  • Big data platforms DATA14003 (5)
  • Inverse problems 1: convolution and deconvolution MAST31401 (5)
  • Combinatorial optimisation CSM12107 (5)
  • Probability theory II MAST31702 (5)
  • Introduction to real and Fourier analysis MAST30132 (5)

Year 2, periods 3 and 4

  • Approximation algorithms CSM12106 (5)
  • Algorithms in genome analysis LSI31007 (5)

Compiled by Antti Kuronen and Ilja Makkonen

The thesis (30) and seminar (5) would be completed in year 2.

Starting in even years

Year 1, periods 1 and 2

  • Introductory Course to Materials Research MATR301 (5)
  • Introduction to data science DATA11001 (5)
  • Introduction to Machine Learning DATA11002 (5)
  • Statistical methods PAP334 (5)
  • Fundamentals of materials physics FYS2084 (5)
  • A course from Aalto University (5)

Year 1, periods 3 and 4

  • Solid state physics: crystal structure and atomic dynamics MATR3031 (5)
  • Solid state physics: electronic structure and properties MATR3032 (5)
  • Basics of Monte Carlo simulations MATR323 (5)
  • Numerical methods in scientific computing MATR322 (10)

Year 2, periods 1 and 2

  • Introduction to molecular dynamics simulations FYS2088 (5)
  • Molecular dynamics simulations MATR325 (5)
  • Courses from other education programmes (10)

Year 2, periods 3 and 4

  • Tools of High Performance Computing MATR326 (5)
  • Monte Carlo simulations in physics MATR324 (5)

Starting in odd years

Year 1, periods 1 and 2

  • Introductory Course to Materials Research MATR301 (5)
  • Introduction to molecular dynamics simulations FYS2088 (5)
  • Statistical methods PAP334 (5)
  • Molecular dynamics simulations MATR325 (5)
  • Fundamentals of materials science FYS2084 (5)

Year 1, periods 3 and 4

  • Solid state physics: crystal structure and atomic dynamics MATR3031 (5)
  • Basics of Monte Carlo simulations MATR323 (5)
  • Solid state physics: electronic structure and properties MATR3032 (5)
  • Tools of High Performance Computing MATR326 (5)
  • Monte Carlo simulations in physics MATR324 (5)

Year 2, periods 1 and 2

  • Introduction to data science DATA11001 (5)
  • Machine Learning 1 DATA11010 (5)
  • Courses from other education programmes (15)

Year 2, periods 3 and 4

  • Numerical methods in scientific computing I MATR3221 (5)
  • Numerical methods in scientific computing II MATR3222 (5)

Compiled by Paolo Muratore-Ginanneschi and Esko Keski-Vakkuri

The thesis (30) and seminar (5) would be completed in year 2.

If you have not studied quantum physics before, please review the courses Quantum Mechanics Ia&b (FYS2061 & FYS2062) before starting in the programme.

Starting in even years

Year 1, periods 1 and 2

  • Mathematical Methods of Physics IIIa TCM304  (5)
  • Mathematical Methods of Physics IIIb TCM305 (5)
  • Scientific computing II FYS2085 (5)
  • Introduction to data science DATA11001 (5)
  • Design and analysis of algorithms CSM12101 (5)
  • Introduction to machine learning DATA11002 (5)
  • Combinatorial optimisation CSM12107 (5)

Year 1, periods 3 and 4

  • Quantum mechanics IIa TCM302 (5)
  • Quantum mechanics IIb TCM303 (5)
  • Advanced course in machine learning DATA12001 (5)
  • Quantum computing FYS2029 (5)
  • Aalto course(s)

Year 2, periods 1 and 2

  • Quantum information A TCM322 (5)
  • Quantum information B TCM323 (5)
  • Open quantum systems I TCM333 (5)
  • Open quantum systems II TCM334 (5)
  • Aalto course(s)

Year 2, periods 3 and 4

  • Randomised algorithms CSM12126 (5)
  • Introduction to the Programming of Quantum Computers CSM14211 (5)

Starting in odd years

Year 1, periods 1 and 2

  • Mathematical Methods of Physics IIIa TCM304 (5)
  • Open quantum systems I TCM333 (5)
  • Design and analysis of algorithms CSM12101 (5)
  • Mathematical Methods of Physics IIIb TCM305 (5)
  • Open quantum systems II TCM334 (5)
  • Scientific computing II FYS2085 (5)

Year 1, periods 3 and 4

  • Quantum mechanics IIa TCM302 (5)
  • Randomised Algorithms CSM12126 (5)
  • Quantum mechanics IIb TCM303 (5)
  • Quantum computing FYS2029 (5)
  • Aalto course(s)

Year 2, periods 1 and 2

  • Quantum Computing and Quantum Information A TCM322 (5)
  • Open classical systems I TCM335 (5)
  • Introduction to data science DATA11001 (5)
  • Quantum Computing and Quantum Information B TCM323 (5)
  • Open classical systems II TCM336 (5)
  • Machine Learning 1 DATA11010 (5)
  • Combinatorial optimisation CSM12107 (5)

Year 2, periods 3 and 4

  • Machine Learning 2 DATA11011 (5)
  • Aalto course(s)

Compiled by Paolo Muratore-Ginanneschi and Esko Keski-Vakkuri

The thesis (30) and seminar (5) would be completed in year 2.

If you have not studied quantum physics before, please review the courses Quantum Mechanics Ia&b (FYS2061 & FYS2062) before starting in the programme.

Starting in even years

Year 1, periods 1 and 2

  • Mathematical Methods of Physics IIIa TCM304 (5)
  • Mathematical Methods of Physics IIIb TCM305 (5)
  • Quantum information A TCM322 (5)
  • Quantum information B TCM323 (5)
  • Combinatorial optimisation CSM12107 (5)

Year 1, periods 3 and 4

  • Quantum mechanics IIa TCM302 (5)
  • Quantum mechanics IIb TCM303 (5)
  • General relativity I PAP348 (5)
  • General relativity II PAP349 (5)
  • Aalto course(s)

Year 2, periods 1 and 2

  • Quantum Field Theory I TCM327 (5)
  • Quantum Field Theory II TCM328 (5)
  • Open quantum systems I TCM333 (5)
  • Open quantum systems II TCM334 (5)
  • Kinetic Theory I TCM325 (5)
  • Kinetic Theory II TCM326 (5)

Year 2, periods 3 and 4

  • Introduction to the Programming of Quantum Computers CSM14211 (5)

Starting in odd years

Year 1, periods 1 and 2

  • Mathematical Methods of Physics IIIa TCM304 (5)
  • Kinetic Theory I TCM325 (5)
  • Open quantum systems I TCM333 (5)
  • Mathematical Methods of Physics IIIb TCM305 (5)
  • Open quantum systems II TCM334 (5)

Year 1, periods 3 and 4

  • Quantum mechanics IIa TCM302 (5)
  • General relativity I PAP348 (5)
  • Quantum mechanics IIb TCM303 (5)
  • General relativity II PAP349 (5)
  • Quantum computing FYS2029 (5)

Year 2, periods 1 and 2

  • Quantum information A TCM322 (5)
  • Quantum Field Theory I TCM327 (5)
  • Open classical systems I TCM335 (5)
  • Quantum information B TCM323 (5)
  • Open classical systems II TCM336 (5)
  • Quantum Field Theory II TCM328 (5)

Year 2, periods 3 and 4

  • Aalto course(s)

Compiled by Kimmo Tuominen and David Weir

The thesis (30) and seminar (5) would be completed in year 2.

Starting in even and odd years

Year 1, periods 1 and 2

  • Introduction to particle physics I PAP332 (5)
  • Cosmology I FYS2081 (5)
  • Introduction to data science DATA11001 (5)
  • Introduction to machine learning DATA11002 (5)
  • Bayesian data analysis LSI35002 (5)
  • Scientific computing II FYS2085 (5)

Year 1, periods 3 and 4

  • Quantum mechanics IIa TCM302 (5)
  • Quantum mechanics IIb TCM303 (5)
  • General relativity I PAP348 (5)
  • General relativity II PAP349 (5)
  • Advanced course in machine learning DATA12001 (5)
  • A course from another programme (5)

Find a thesis topic and supervisor

Year 2, periods 1 and 2

  • Computational statistics MAST32001 (5)
  • Cosmology II PAP326 (5)
  • Quantum field theory I TCM327 (5)
  • Quantum field theory II TCM328 (5)
  • A course from another programme (5)

Year 2, periods 3 and 4

The final semester is dedicated to the seminar and completing the thesis.
More about the programme