Marie Skłodowska-Curie COFUND Early Stage Researcher position: Computational Physics

Marie Skłodowska-Curie COFUND Early Stage Researcher, Neuro-Innovation: Computational Physics

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034307.


Image reconstruction in high temporal resolution functional MRI of the brain


Doctoral Programme in Science, Technology and Computing (SCITECO)


Professor Ville Kolehmainen, Department of Applied Physics (primary supervisor)
Professor Olli Gröhn, A.I. Virtanen Institute for Molecular Sciences
Associate Professor Tero Montonen, Business School


Department of Applied Physics, Computational Physics and Inverse Problems Group

The Computational Physics and Inverse Problems research group focuses on the fields of inverse problems and mathematical modelling. The group belongs to the Academy of Finland’s Centre of Excellence (CoE) on Inverse Modelling and Imaging. The research portfolio covers various tomographic imaging approaches as well as general topics related to Bayesian and computational inverse problems. In addition, mathematical modelling of different physical systems and modelling of uncertainties is studied. The group consists of several research teams with application fields in biomedical and industrial inverse problems as well as inverse problems in geophysical and atmospheric sciences. The team of Prof. Kolehmainen is focused on development of mathematical models and computational inverse problems methods for image reconstruction in medical imaging techniques such as X-ray tomography, PET and MRI.

The PhD research will be carried out jointly with the Biomedical MRI research group at the A.I. Virtanen Institute for Molecular Sciences.


This research topic needs the participation of people with passion for excellence in research, innovation, and impact. We look for candidates with a background in physics, engineering or applied mathematics. The PhD candidate needs strong programming skills (e.g. MatLab or Python) and previous experience on inverse problems, medical image computing and MRI is highly beneficial.  In addition, high level of English proficiency is essential. As far as soft skills are concerned, we welcome candidates with a proactive, collaborative attitude who are enthusiastic about working and sharing expertise in interdisciplinary teams. Excellent candidates actively participate in various intersectoral activities offered by the PhD programme, and are willing to spend some time abroad with our international academic partners and outside the university at our non-academic partner institutions.


Applied physics, medical imaging, inverse problems, neurosciences, biomedical engineering, applied mathematics, computational physics


Functional magnetic resonance imaging (fMRI) is a 4D imaging technique which is used to study (hemodynamic) response of the brain to a visual, physical or electrical stimulus. In a typical fMRI experiment, a complete set of MRI measurement data is collected over several repetitions of the stimulus, making the mathematical inversion of the MRI measurement data to the 4D fMRI image solvable with standard fMRI image computation techniques. In this research topic, we aim to develop a novel fMRI protocol for imaging response of the brain to random trigger signals. In such an experiment, only a very few random repetitions of the trigger signals may be available, leading to a very sparse and random sampling of the MRI measurement data space, requiring advanced computational inverse problems methods and imaging approaches for the solution of the 4D fMRI image. The objective of the PhD research is to develop a novel combination of advanced (compressed sensing based) image reconstruction methods and optimal data collection techniques, such as radial zero-echo time fMRI, which can produce high temporal and spatial resolution fMRI with random repeated stimulus. The research can result in major advances in functional imaging techniques of the brain, paving way for breakthroughs in basic research. Approaches developed are expected to also have high translational potential to human imaging and high commercialization value. Therefore, constant evaluation and training for this aspect is integral part of the project. 


Publications related to the PhD topic:

Rasch J, Kolehmainen V, Nivajärvi R, Kettunen M, Grohn O, Burger M. and Brinkmann E-M. “Dynamic MRI reconstruction from undersampled data with an anatomical prescan” INVERSE PROBLEMS 34 7: 074001 (2018).

V-V Wettenhovi, V. Kolehmainen, J. Huttunen, M. Kettunen, O. Gröhn and M. Vauhkonen. ”State estimation with structural priors in fMRI”. Journal of Mathematical Imaging and Vision, (2017).

Hanhela, M., Kettunen, M., Gröhn, O. et al. Temporal Huber Regularization for DCE-MRI. J Math Imaging Vis 62, 1334–1346 (2020).

Hanhela M, Gröhn O, Kettunen M, Niinimäki K, Vauhkonen M, Kolehmainen V. Data-driven regularization parameter selection in dynamic MRI. J Imaging 7:38, 2021.

Paasonen J, Laakso H, Pirttimäki T, Stenroos P, Salo RA, Zhurakovskaya E, Lehto LJ, Tanila H, Garwood M, Michaeli S, Idiyatullin D, Mangia S, Gröhn O. Multi-band SWIFT enables quiet and artefact-free EEG-fMRI and awake fMRI studies in rat. Neuroimage. 2020 Feb 1;206:116338. doi: 10.1016/j.neuroimage.2019.116338. Epub 2019 Nov 12. PMID: 31730923; PMCID: PMC7008094.

Montonen, T., Eriksson, P. and Woiceshyn, J. It’s not a lonely journey: research collaboration strategies for knowledge production with allies. Academy of Management Learning and Education, online (2021).

Research projects related to the PhD topic:

Academy of Finland, Centre of Excellence in Inverse Modelling and Imaging


The PhD topic is in the interface of imaging, mathematics, physics, engineering and neuroscience. The PhD research will be co-supervised by a team of three scientists with expertise in neuroscience, neuroimaging, MRI, medical image computing, inverse problems and business studies (commercialization).

To advance multi/interdisciplinary collaboration, three Summer Schools will be organised jointly by the Neuro-Innovation supervisors and non-academic partners. In these, multi/interdisciplinarity and intersectoral exchange will be implemented via hands-on interaction between PhD students, supervisors, partners and other stakeholders. The students will participate among other in the following interdisciplinary courses:

  • Neuro-data Hackathon, 3 ECST credits. Open and big data.
  • Neuro-Innovation Living Lab, 3 ECST credits. Entrepreneurial processes and commercialisation paths.

Virtual Platform for multi/interdisciplinary interaction will connect all PhD students in this programme.


The academic partner organizations (see the Neuro-Innovation webpage) will collaborate closely with us in PhD training and students are strongly encouraged to include a secondment and visits with these partners in their studies. During shorter visits (1-4 weeks), you will learn more about research and methods and build international networks. During secondments, you will work on their research project under the supervision of the co-supervisor from the hosting organisation and utilise their infrastructure.  You can also attend courses, seminars, and other events in the host organisations.

The PhD student has the opportunity to visit  the Center for Magnetic Resonance Research at the University of Minnesota or Center for Medical Image Computing, University College London for a period that is jointly agreed. This will enable the candidate to familiarize with state-of-the-art methodologies in functional MRI, medical image computing and computational inverse problems.


The Societal Impact Board of this PhD programme with 14 intersectoral partners will collaborate closely with us in PhD training, for instance on the following activities:

  • Neuro-Innovation Talent Hub: monthly gathering with special guests (e.g., researchers, professionals, business experts, stakeholders) and discussions about research topics and career prospects.
  • Neuro-Innovation Boot Camp: yearly competition concerning the utilisation of research results.


High quality research and publications of excellence are a way to solve societal problems and challenges. This vision is shared by the collaborating partners in this doctoral program, and involvement with various stakeholders will allow the pursuit of a goal with potential for brain health innovation. The University of Eastern Finland pursues societal impact that goes beyond academia focusing on the transformation of society, leading to fairer and more diverse societies, where inclusive social development and welfare are enhanced. The objective of the PhD research is to advance functional imaging of the brain, paving way for breakthroughs in basic research of the brain. Approaches developed are expected to also have high translational potential to human imaging and high commercialization value. Therefore, constant evaluation and training for this aspect is integral part of the PhD research. 


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