DREAM PROJECTS 2nd call

DREAM will educate in total 100 doctors located at seven Finnish universities in a diverse and multidisciplinary setting, encompassing applied mathematics, physics, engineering, and applied sciences. The doctoral researchers are trained by the experts on fields related to the Flagship of Advanced Mathematics for Sensing, Imaging and Modelling (FAME) in close interaction with companies and other sectors of the society.

The projects available in the second call at the University of Oulu are described below. A link for submitting applications and further information about the positions is provided at the end of the text.

1. RU Mathematical Sciences: Low X-ray Exposure for Direct Tumor Surface Estimation and Imaging Optimization

Supervisors: Babak Maboudi Afkham & Andreas Hauptmann

This project aims to directly estimate tumor geometric attributes from X-ray CT data, bypassing traditional image reconstruction steps. By eliminating reconstruction errors, we can improve the accuracy of these key attributes, crucial for cancer detection, diagnostics, and treatment planning. Additionally, we will develop a personalized imaging setup to optimize accuracy and certainty for specific attributes, potentially reducing radiation exposure while enhancing clinical outcomes.

2. RU Mathematical Sciences: Advanced image reconstruction for low-field magnetic resonance imaging

Supervisor: Andreas Hauptmann

In low-field MRI many ideal assumptions of the high-field equivalent are not satisfiedand hence advanced reconstruction methods are necessary to obtain satisfactory reconstructions. In this project, the candidate is expected to perform fundamental research on advanced image reconstruction techniques while taking into account the nonlinear nature of MR signal generation. A combination of classic model-based reconstructions and data-driven approaches will be developed.

3. RU Mathematical Sciences: Effectiveness of audiovisual material for teaching and outreach for mathematics of medical image reconstruction

Supervisor: Andreas Hauptmann

We have seen an increase in the use of audiovisual material for educational purposes in recent years. There are some notable examples for teaching advanced concepts of mathematics, but there is still little insight on the learning outcomes specific for applied mathematics. This project aims to create teaching and outreach material for applied mathematic with respect to medical image reconstruction and study its effectiveness in teaching.

4. RU Applied and Computational Mathematics: Novel computational methods for nonlinear wave inversion

Supervisor: Teemu Tyni

The main focus of this research project is on development of novel reconstruction methods employing nonlinear self-interaction of waves. Mathematical theory will be developed to guarantee stability of the numerical results. Example applications include thermo-acoustic imaging and quantitative elastography, where nonlinear phenomena can be used to produce new data.

5. RU Health Sciences and Technology: Raman spectroscopy for better diagnosis of rheumatic diseases

Supervisor: Simo Saarakkala

The diagnosis of rheumatic diseases presents a great challenge in the primary care due to the absence of clear clinical, radiographic, laboratory, or pathological benchmarks for diagnosis. It is well established that Raman spectroscopy is an effective, label-free technique for characterizing the biomolecular composition of biological samples. In this project, we will investigate the potential of Raman spectroscopy for the detection of rheumatic diseases in human blood serum samples, with the aim of developing an advanced mathematical model for disease classification and prognosis prediction.

6. RU Health Sciences and Technology: Monitoring brain radiotherapy treatment response with multiparametric MRI

Supervisor: Juha Nikkinen

The aim of the project is to find both qualitative and quantitative multiparametric MRI parameters to assess brain tumor biology, progression and treatment efficacy. We will also investigate the parameters as a prognostic biomarkers for predicting and evaluating long-term radiation therapy treatment effects to brain function and tumor relapse.

7. RU Space Physics and Astronomy: Advanced methods for 3D observations of the ionosphere with the EISCAT3D radar

Supervisor: Ilkka Virtanen

The project will include use and development of advanced incoherent scatter radar data analysis tools for novel observations of the ionosphere with the EISCAT3D radar system. The radar observations will be combined with data from other ground-based and satellite instruments to study particle precipitation, energy transfer processes, and plasma flows in the auroral magnetosphere-ionosphere-thermosphere system.

Please apply here: https://oulunyliopisto.varbi.com/what:job/jobID:748647/