DREAM announces nine doctoral researcher positions at the University of Oulu

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 first call at the University of Oulu are:

1. RU of Mathematical Sciences: Scalability of learned reconstructions for cone-beam computed tomography and applications in medicine
Supervisor: Andreas Hauptmann

Learned reconstructions for 3D cone-beam computed tomography (CBCT) require significant hardware resources for training as well as evaluation. In this project the candidate is expected to develop novel methods that circumvent the high hardware requirements by combining operator splitting approaches for projection geometries with novel deep learning approaches.

2. RU of 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 of Health Sciences and Technology: One-stop shop MRI: AI-assisted combined clinical and quantitative MRI for comprehensive tissue characterization
Supervisor: Miika Nieminen

The project will focus on developing new MRI methodologies, including pulse sequence and data analysis methods to simultaneously produce clinical and quantitative MRI images for diagnostics and comprehensive characterization of musculoskeletal tissues in particular. The project involves collaboration with other academic partners as well as an MRI manufacturer.

4. RU of Health Sciences and Technology: Future multi-spectral CT: reconstruction, artefact reduction and tissue quantification algorithm development with diagnostic scanners
Supervisor: Miika Nieminen

In this project, novel image reconstruction algorithms and photon counting detector technology are developed to optimize dentomaxillofacial and cranial computed tomography and cone-beam CT by developing novel computational methods for reducing beam hardening and metal artifacts in the dentomaxillofacial region, exploit reconstruction algorithms to boost image quality and develop multi-spectral reconstruction techniques to enhance image quality in helical stroke CT.

5. RU of Health Sciences and Technology: Accelerate and enhance image quality in cardiac MRI
Supervisor: Timo Liimatainen

The project will include development of pulse sequences, data-analysis, image processing and testing novel methodology using clinical and/or experimental MRI devices. The project will be done in close collaboration with MRI vendor.

6. RU of Health Sciences and Technology: Methods to predict and search radiation dose outliers in medical imaging
Supervisor: Matti Hanni

Statistical and inversion-based methods will be utilized to predict, search, and possibly also prevent radiation dose outliers in medical imaging employing ionizing radiation. The emphasis will be on computed tomography, mammography, and radiography, in this order.

7. RU of Health Sciences and Technology: Enabling high-resolution hierarchical imaging of musculoskeletal tissues
Supervisor: Mikko Finnilä

X-ray microscopy and micro-computed tomographic imaging are powerful tools to study biological tissues. However, current technology is based on physical magnification schemes settings boundaries between sample size and achievable resolution. In this project we will implement novel reconstruction methods experimental tomographic data to allow imaging of large objects with high resolution.

8. RU of Space Physics and Astronomy: Space weather influence on ionospheric dynamics
Supervisor: Heikki Vanhamäki

We will investigate dynamics of the auroral ionosphere, especially the auroral currents systems, and how they are driven by space weather disturbances such as geomagnetic storms. Large ground- and satellite-based datasets will be utilized together with machine learning tools.

9. NMR Research Unit: Novel NMR methods for analyzing chemical structure and composition of rubber compounds
Supervisor: Ville-Veikko Telkki

We develop novel NMR methods for analyzing chemical structure and composition of rubber compounds. The NMR methods rely on relaxation contrast, which reflect molecular mobility and chemical environment. The methods are feasible with affordable and portable benchtop spectrometers, also including single-sided instruments, which allow probing surfaces of objects without size or geometry restrictions, even for entire tires. Industrial rubber products are usually more complex than the formulations used in most previous research, and the large amount of various compounding ingredients makes NMR results difficult to analyze. This challenge is addressed in the proposed doctoral project. Vulcanization time and changes in raw materials affect chemical structure and mobility of elastomers in rubber compound. We aim to find novel and more precise means to quantify different network structures and important parameters such as crosslink density from rubber compounds as well as from tires. Furthermore, we study how novel sustainable raw materials affect to vulcanizate structure. An important part of the project is also developing mathematical processing models and inversion algorithms of the relaxation data.

Please apply here: https://oulunyliopisto.varbi.com/en/what:job/jobID:709262/