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 allocated an academic mentor who will help you make your final choice.
Example study paths for TCM

The primary study goal in the TCM programme 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.
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).

Year 1, periods 1 and 2

  • Introduction to biological physics MATR331 (5)
  • Molecular electronic structure KEM369 (5)
  • Introduction to structural biology and biophysics GMB105 (5)
  • QM and QM/MM modeling of bio/chemical systems TCM324 (5)

Year 1, periods 3 and 4

  • Molecular modeling KEM342 (5)
  • Physics of biological systems MATR332 (5)
  • Molecular symmetry and group theory KEM378 (5)
  • Basics of Monte Carlo simulations MATR323 (5)
  • Numerical methods in scientific computing MATR322 (10)

Year 2, periods 1 and 2

  • Modeling of biological systems MATR333 (5)
  • Nanophysics and Nanochemistry MATR305 (5)
  • Molecular dynamics simulations MATR325 (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)

Compiled by Lucile Turc

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

Year 1, periods 1 and 2

  • Plasma physics PAP304 (5)
  • Space applications of plasma physics PAP305 (5)
  • Scientific computing II FYS2085 (5)
  • Introduction to machine learning DATA11002 (5)
  • Introduction to data science DATA11001 (5)
  • Data analysis with Python AYCSM90004en (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 TCM309 (10)
  • Advanced statistical physics TCM306 (5)

Year 2, periods 3 and 4

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

Compiled by Kai Puolamäki

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

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 MAST31132 (5)

Year 2, periods 3 and 4

The final periods are dedicated to writing the thesis.

Compiled by Antti Kuronen

Not all courses are taught every year. Two example study paths are therefore shown, one starting in even years (2024, 2026), and the other in odd years (2025). There are several suitable courses offered by Aalto University, examples of these are given at the end.

Starting in even years

In this study path the thesis would be started in periods 3-4 of Year 1. The seminar would be taken in periods 3-4 of Year 2.

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 MATR303 (10)
  • 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

In this study path the thesis would be started in periods 3-4 of Year 1. The seminar would be taken in periods 3-4 of Year 2.

Year 1, periods 1 and 2

  • Introductory Course to Materials Research MATR301 (5)
  • Molecular dynamics simulations MATR325 (10)
  • Introduction to Machine Learning DATA11002 (5)
  • Fundamentals of materials physics FYS2084 (5)
  • A course from Aalto University (5)

Year 1, periods 3 and 4

  • Solid state physics MATR303 (10)
  • Basics of Monte Carlo simulations MATR323 (5)
  • Monte Carlo simulations in physics MATR324 (5)
  • Quantum mechanics IIa TCM302 (5)
  • Quantum Mechanics IIb TCM303 (5)

Year 2, periods 1 and 2

  • Introduction to data science DATA11001 (5)
  • Courses from other education programmes (10)

Year 2, periods 3 and 4

  • Numerical methods in scientific computing MATR322 (10)

Aalto University courses

For up to date information contact e.g. Antti Kuronen.

  • Density Functional Theory for Practitioners MATR374 (5)
  • Density Functional Theory for Experts MATR375 (5)
  • Machine Learning for Materials Science MATR376 (5)

Compiled by Paolo Muratore-Ginanneschi

Note: Aalto University and University of Helsinki have signed an agreement on “Cooperation on quantum technology education in the Helsinki metropolitan area” (9/2021). This agreement allows students from both universities to take cross-study certain courses related to quantum technology. You can find further information here as well as on the InstituteQ (Finnish National Quantum Institute) webpage. The study suggestions below contain Aalto courses that are available to you through this agreement.

See also our old wiki pages for additional examples that are not yet on this website.

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

guided self study before starting in the programme: FYS2018 Quantum mechanics I (5)

Year 1, periods 1 and 2

  • Scientific computing II FYS2085 (5)
  • Introduction to data science DATA11001 (5)
  • Design and analysis of algorithms CSM12101 (5)
  • Introduction to machine learning DATA11002 (5)

Year 1, periods 3 and 4

  • In­tro­duc­tion to the Pro­gram­ming of Quantum Com­puters CSM14211 (5)
  • Randomized algorithms I CSM12104 (5)
  • Advanced course in machine learning DATA12001 (5)
  • Quantum computing FYS2029 (5)
  • Quantum mechanics IIa TCM302 (5)

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)
  • Combinatorial optimisation CSM12107 (5)

Year 2, periods 3 and 4

  • Randomized algorithms II CSM12105 (5)
  • Quantum mechanics IIb TCM303 (5)

Compiled by Paolo Muratore-Ginanneschi

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

guided self study before starting in the programme: FYS2018 Quantum mechanics I (5)

Year 1, periods 1 and 2

  • Quantum Field Theory I TCM327
  • Quantum Field Theory II TCM328
  • Kinetic Theory I TCM325
  • Kinetic Theory II TCM326

Year 1, periods 3 and 4

  • In­tro­duc­tion to the Pro­gram­ming of Quantum Com­puters CSM14211 (5)
  • Quantum mechanics IIa TCM302 (5)
  • Quantum mechanics IIb TCM303 (5)
  • Many-body Quantum Mechanics (at Aalto University, PHYS-E0420) (5)
  • Quantum Circuits (at Aalto University, PHYS-C0254) (5)

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)
  • Combinatorial optimisation CSM12107 (5)

Year 2, periods 3 and 4

  • Solid-State Physics (at Aalto University, PHYS-E0421) (5)
  • Philo­sophy of Science, Ad­vanced (FILM-312) (5) [check prerequisites]

 

Compiled by Kimmo Tuominen and David Weir

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

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 PAP348 (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.