Early Stage Researcher, 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 of Molecular Medicine (DPMM)

Research Director Alejandra Sierra Lopez, primary supervisor
Prof. Jussi Tohka
Prof. Ville Kolehmainen
Dr. Outi-Maaria Palo-oja

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

The multiscale imaging group is a multidisciplinary team interested in the application, combination and development of imaging modalities focused on the brain at several scales. Our greatest interest is the study of the structure of the healthy and diseased brain from the macro- and microscale with non-invasive techniques, such as magnetic resonance imaging (MRI), to the nanoscale with microscopy techniques, including 2D and 3D light and electron microscopy. Our 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. The research profile for the Sierra Lab.

Rapid advances in non-invasive neuroimaging methods have revolutionized the possibilities to study changes occurring in living brains 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 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 other more applied topics. The 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 the 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. The research profile for the Kolehmainen Lab.

We welcome highly motivated candidates with an engineering, computer science or physics background interested in working beyond the boundaries of their own background. The project requires a person who is enthusiastic to learn new skills from different fields of science, and more importantly, to integrate the knowledge to the field of neuroimaging and neuroscience. The ideal candidate has experience and/or studies in signal processing, image processing or machine learning and possesses strong programming experience in Matlab and/or Python. A high level of written and spoken English is essential. Additionally , good communication skills are necessary in order to work in a research team with members from different scientific backgrounds. A high degree of independence and initiative is desirable .

This research project needs the participation of people with passion for excellence in research, innovation, and societal impact. We are looking for candidates who seek to contribute to brain health innovation research with a keen eye on the application of research knowledge to the problems and challenges of society and industry. Ideal candidates want to open their minds to intersecting and complementary fields of science, seeking a multi/interdisciplinary approach in research. The candidates should have a desire to approach the brain health industry and other organizations that will benefit from science when developing services, products, and processes.

Overall, the candidate must be willing to combine complementary areas of knowledge in neurosciences, data sciences, social sciences, applied physics and/or law to complete the thesis project. Moreover, the candidate is required to have good methodological skills, especially in longitudinal analyses. 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 multi- and interdisciplinary ageing research, and are willing to spend some time abroad with our international partners and outside the university in our partner institutions.

This project will aim at improving the state-of-the-art in magnetic resonance imaging (MRI) by simulations based on advanced histological imaging and cutting-edge data-analysis techniques. Brain diseases demand more sensitive and specific neuroimaging technologies to improve the clinical diagnostics and prognostics. Emerging MRI acquisition schemes have potential to extract detailed information of the brain tissue, sensitive to events occurring on a scale much smaller than the image voxel size. However, these techniques require proper interpretation of which tissue structures and processes produce the MRI signal. Advanced three-dimensional (3D) histological techniques offer new windows to explore the brain tissue and its pathology. More importantly, the introduction of novel data-analysis and machine learning methodologies into tissue modelling and analysis of microscopy 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. The outcomes of this project 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 candidate can also suggest their own topic or modify this topic in their motivation letter.


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. doi.org/10.1007/s10851-020-00985-2.

Research projects related to the PhD topic:

  • Academy of Finland, Erkko Foundation, Centre of Excellence in Inverse Modelling and Imaging (for details, see the webpages of the supervisors).


The research topic is in the interface of imaging, mathematics, physics, computer science, neuroscience and innovation. The PhD project will be co-supervised by a team of three scientists with expertise in neuroscience, neuroimaging, image processing, mathematical modelling and innovation management.

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.

  • 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, among others, will collaborate closely with us in the 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 a secondment, 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 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.

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

Apply the position

This project aims to validate and interpret 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 for 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 project will offer new windows for the interpretation of non-invasive imaging methods with a potential of becoming diagnostic 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.

The University of Eastern Finland is the most multidisciplinary university in Finland. We are home to 15,500 students and 2,700 staff members. Our research is ranked among the best in the world in several fields (inc. forest sciences). We generate research-based knowledge and make it openly accessible for the benefit of all. UEF stands for action with impact that is relevant today and tomorrow. To learn more about our university please visit our website at www.uef.fi/en.