Marie Skłodowska-Curie COFUND Early Stage Researcher position: Neuroscience (Multiscale Imaging)

Marie Skłodowska-Curie COFUND Early Stage Researcher, Neuro-Innovation: Neuroscience (Multiscale Imaging)

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.


Computational validation of advanced MRI


Doctoral Programme in Molecular Medicine (DPMM)


Research Director Alejandra Sierra Lopez (primary supervisor)
Prof. Jussi Tohka
Prof. Ville Kolehmainen
Dr. Eeva Aromaa


The PhD research will be conducted at A. I. Virtanen Institute for Molecular Sciences, Faculty of Health Sciences in collaboration with the Department of Applied Physics, Faculty of Science and Forestry, and the UEF Business School.

The multiscale imaging group is a multidisciplinary team interested in the application, combination and development of imaging modalities focused in brain at several scales. The greatest interest of the group is the study of the structure of the healthy and diseased brain from the macro-, micro- and nanoscale with non-invasive techniques, such as magnetic resonance imaging (MRI), to the nanoscale with microscopy techniques, including 2D and 3D light and electron microscopy. The group combines image processing, data analyses and tissue modelling to characterize, validate and develop cutting-edge MRI technology to improve the non-invasive detection of tissue patterns in the brain during disease.

Research profile for the Sierra lab

Rapid advances in non-invasive neuroimaging methods have revolutionized the possibilities to study changes occurring in living brain across a variety of time-scales ranging from seconds to an entire life span. A large part of these advances can be attributed to the development of dedicated computational algorithms, which are essential in extracting quantitative information from images. The Biomedical Image Analysis group develops such computational methods to analyze the brain imaging data. A major part of our research concentrates around machine learning. In particular, we study imaging-based predictive modeling of brain diseases and neural network-based techniques for image analysis. This includes fundamental work in areas of feature selection, multiview learning, and transfer learning among with more applied topics.  

Research profile for the Tohka lab

The Computational Physics and Inverse Problems research group resides at the Department of Applied Physics, UEF. The 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 tomography imaging techniques such as X-ray tomography,  PET and MRI.

Research profile for the Kolehmainen lab


We welcome highly motivated candidates with an engineering, computer science or physics background interested to integrate their knowledge to the field of neuroimaging. The ideal candidate has experience and/or studies in signal processing, image processing or machine learning, solid background in physics and/or mathematics, and possess strong programming skills in Matlab and/or Python. High level of written and spoken English is essential. Also, good communications skills to work in a research team with members with different scientific background, and high degree of independent and initiative.

The candidate must be willing to combine complementary areas of knowledge in neurosciences, data sciences, and applied physics focused in brain health innovation and commercialization. As far as soft skills are concerned, we welcome candidates with a proactive, collaborative attitude, and who are enthusiastic about building, creating, and working in teams. Excellent candidates desire to participate in activities concerning the commercialization of neuroscience research, and spend some time abroad with our international partners and outside the university in our partner institutions.


Neurosciences, computer sciences, physics, mathematics, neuroimaging


Brain diseases demand more sensitive and specific neuroimaging technologies to improve the clinical diagnostics and prognostics. Emerging magnetic resonance imaging (MRI) acquisition schemes have potential to extract detailed information of the brain tissue, sensitive to events occurring in a scale much smaller than the image voxel size. However, these techniques require a proper interpretation of which tissue structures and processes produce the MRI signal. The introduction of novel data-analysis and machine learning methodologies into tissue modelling and analysis of advanced three-dimensional (3D) histological dataset proposes novel ways for the interpretation of MRI. The integration of tissue information into the validation of MRI techniques will require a multidisciplinary approach including imaging, mathematics, computer science and neuroscience. This PhD topic will aim to improve the state-of-the-art in MRI by simulations based on advanced histological imaging and cutting-edge data-analysis techniques. This PhD research will benefit several fields of science, from our understanding of MRI at the tissue level to the development of novel tools for tissue modeling and computational analysis methods. The research topic will have a high impact in the development of imaging technology for the brain in basic research with highly potential of translational value for human imaging and commercialization.


Publications related to the PhD topic:

Abdollahzadeh A, Belevich I, Jokitalo E, Tohka J, Sierra A. Automated 3D Axonal Morphometry of White Matter. Sci Rep. 2019 Apr 15;9(1):6084. doi: 10.1038/s41598-019-42648-2.

Abdollahzadeh A, Belevich I, Jokitalo E, Sierra A, Tohka J. DeepACSON automated segmentation of white matter in 3D electron microscopy. Commun Biol. 2021 Feb 10;4(1):179. doi: 10.1038/s42003-021-01699-w.

Salo RA, Belevich I, Jokitalo E, Gröhn O, Sierra A. Assessment of the structural complexity of diffusion MRI voxels using 3D electron microscopy in the rat brain. Neuroimage. 2021 Jan 15;225:117529. doi: 10.1016/j.neuroimage.2020.117529.

De Feo R, Shatillo A, Sierra A, Valverde JM, Gröhn O, Giove F, Tohka J. Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases. NeuroImage. 2021 Apr 1;229:117734. doi: 10.3389/fnins.2020.610239.

Hanhela M, Kettunen M, Gröhn O, Vauhkonen M, Kolehmainen V. Temporal Huber Regularization for DCE-MRI. J Math Imaging Vis. 2020 62, 1334–1346.

Research projects related to the PhD topic:

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


The research topic is in the interface of imaging, mathematics, physics, computer science and neuroscience. The PhD topic will be co-supervised by a team of four scientists with expertise in neuroscience, neuroimaging, image processing and mathematical modelling focused in brain health innovation and 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 e.g. for the following interdisciplinary courses:

  • Neuro-ethics and patient rights, 3 ECTS credits. Research ethics, patient rights, data security
  • 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, e.g. Lund University or University Medical Center Freiburg, between others, 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 topic 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 Societal Impact Board of this PhD programme with intersectoral partners will collaborate 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.

There is a particularly close collaborative relationship planned with Kuopio University Hospital and Charles Rivers in the proposed topic.


This PhD research aims to validate and interpret the imaging contrast through simulations using realistic models generated from brain tissue. By introducing realistic tissue models, we will create a unique opportunity to associate distinct tissue features responsible of the contrast in MRI. Beyond the validation of MRI, the realistic tissue models in combination with simulations can serve as a base to improve and develop cutting-edge MRI methodologies. More importantly, this PhD research will offer new windows for the interpretation of non-invasive imaging methods with a potential of becoming diagnostics tools in the clinic.

High quality research and publications of excellence are a way to solve scientific and 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 UEF policy on gender equality and equal opportunities is based on Finnish legislation and the values of the university. The goal of gender equality and equal opportunities at the university is to identify and prevent expressions, structures and functions that maintain or increase inequality and to promote gender equality and equal opportunities at all levels. The university has a Gender Equality and Equal Opportunities Programme, which describes the measures intended to implement and promote gender equality and equal opportunities among staff and students. The university takes an active approach to promoting equal opportunities and acting against discrimination.


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