Doctoral Education Pilot for Mathematics of Sensing, Imaging and Modelling

Here you can find more information about the Doctoral Education Pilot for Mathematics of Sensing, Imaging and Modelling as well as the list of potential supervisors in the pilot. If you're interested in applying for a position in the thematic field of the pilot, you should contact a possible supervisor from this list as soon as possible.

The mathematics of inverse problems aims to extract meaningful information from noisy and incomplete measurement data. It is a field strongly driven by applications. Many mathematical frameworks are useful in inversion, for example analysis, probability, functional analysis, linear algebra and geometry. Computational methods are based on numerical linear algebra, optimization and neural network models. 

We are looking for doctoral researchers for 3-year doctoral positions to work on the following themes:

  • digital geography (Professor Tuuli Toivonen)
  •  enhanced seismic surface wave imaging using deconvolution strategies  (Professor Gregor Hillers)
  • gamma ray imaging for nuclear safety, security and safeguards (Professor Peter Dendooven)
  • medical imaging, including X-ray tomography and electrical impedance tomography (Professors Matti Lassas and Samuli Siltanen, Lecturer Petri Ola)
  • satellite observation analysis (Dr. Johanna Tamminen, FMI)
  • AI methods for meteorology (Dr. Marko Laine, FMI)
  • probabilistic methods in inverse problems (Professor Lauri Oksanen, Lecturer Petteri Piiroinen)

Due to the general nature of mathematics, other areas of application can also be considered.

Suitable candidates have a background in mathematics; experience in applications and scientific programming is a plus. It is also possible to apply if the candidate comes from some of the relevant fields of application and has the motivation to learn the necessary mathematics during the studies. 

For more information on this research area, see the following pages:

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  • I develop gamma ray imaging instrumentation for various applications. Data processing and analysis techniques, including image reconstruction, are developed for state-of-the-art equipment. Monte Carlo simulations are an important research tool.

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  • We use dense array data of the seismic noise field to image the subsurface structure. Our most recent findings demonstrate the effectiveness and resolution power of a new autocorrelation approach developed with acousticians and medical imaging experts.

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  • We develop statistical methods for assessing treatment effectiveness using large data obtained by combining national-level registers. Our methods account for complex data generating processes. We work with wide range of real problems.

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  • I study applied mathematics. Focus areas are imaging, modelling and mathematics of quantum mechanics, including quantum computing.

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  • Inverse problems for partial differential equations and related geometric questions. Both theoretical and computational aspects are considered.

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  • My main focus is in the use of methods of partial differential equations to inverse problems, especially the interplay between electrodynamcis and medical imaging. The most important discoveries are related to impedance imaging.

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  • Quasiconformal and metric geometry with applications to e.g. inverse problems.

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  • We study topics that are part of inverse problems and mathematical statistics especially from Bayesian point-of-view. The main focus is in models coming from stochastic analysis and recently the methods have been used for asset price bubbles detection.

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  • I develop mathematically solid algorithms for inverse problems and imaging. The main application areas are medical imaging (X-ray tomography, electrical imaging) and non-destructive testing (passive gamma-ray imaging of spent nuclear fuel).

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  • We focus on understanding people’s mobility at various spatial and temporal scales. We use big, open data and advanced analytics to study interactions between people and people and their environments, through the lens of mobility.

Peter Dendooven

  • Passive Gamma Emission Tomogragphy of spent nuclear fuel
  • Artificially intelligent gamma ray imaging detectors

Dario Gasbarra

  • Mathematical and Statistical modeling and analysis of diffusion-MRI data

Gregor Hillers

  • Deconvolution in seismic focal spot imaging; application to dense array data (data acquisition completed before thesis starts)
  • Deconvolution in seismic focal spot imaging; theoretical and numerical analysis

Marko Laine

  • Statistical data fusion methods for meteorological and climate data
  • Machine learning methods for energy weather forecasts
  • Machine learning in post processing  weather forecasts
  • AI methods for forecasting dynamical systems with meteorological applications

Matti Lassas

  • Machine learning algorithms in geometric dimensionality reduction and applications to diffusion and wave tomography

Lauri Oksanen

  • Inverse problems with combined measurements
  • Finite element methods for ill-posed problems

Petri Ola

  • Inverse and Direct Scattering from Thin Metamaterial Layers

Petteri Piiroinen

  • Probabilistic interpretation of cloaking

Samuli Siltanen

  • Electrical impedance tomography (EIT) applied to classification and monitoring of stroke. The work will specifically focusing on the use of complex geometric optics solutions to EIT
  • Perception-aware inversion. Developing method for assessing image quality from the point of view of human visual system and end-user needs. This is a collaboration with psychologists
  • X-ray tomography for breast cancer specimens using photon counting detectors (color X-ray cameras). This project is a collaboration with an industrial partner of the FAME Flagship

Johanna Tamminen

  • Inverse problems and data analysis of novel high resolution satellite observations of greenhouse gases and air pollution
  • Non-linear inversion and Bayesian uncertainty quantification of space-based greenhouse gas observations 
  • Data-driven and ML-guided estimation of greenhouse gas fluxes using satellite observations
  • AI methods for analysing anomalies and changes in satellite observations 

Tuuli Toivonen

  • Modelling / denoising mobility patterns derived from big data sources: Global & crossborder mobilities
  • Modelling / denoising mobility patterns derived from big data sources: Urban and natural landscapes
  • Learning from population dynamics derived from mobile data sources: sythetic population for mobility/health/sustainability/equity analyses