1 Doctoral Candidate in Recursive Bayesian Estimation for Medical and Smartphone Technology in Finland | Aalto University

We are looking for a doctoral candidate to the field of Recursive Bayesian Estimation for Medical and Smartphone Technology at the Sensor Informatics and Medical Technology group of Department of Electrical Engineering and Automation (EEA), Aalto University. The work will be done under guidance of Professor Simo Särkkä in close collaboration with Department of Neuroscience and Biomedical Engineering (NBE).

The task of the candidate is to develop and apply Bayesian filtering and smoothing methods such as particle filters and non-linear Kalman filters, and methods like Markov chain Monte Carlo (MCMC) to medical and smartphone applications. In particular, the candidate is expected to contribute to the Academy of Finland project “Dynamic identification of functional brain networks by Bayesian tracking of electrophysiological data”. The aim in that project is to develop particle filtering and related Bayesian computational methods for brain network analysis using electroencephalography (EEG) and magnetoencephalography (MEG) data.

In addition to the abovementioned project, the candidate will also have the chance to develop and apply recursive Bayesian methodology to other biomedical applications, and to smartphone applications such as indoor localization and SLAM – and probably to other applications as well. These emerging topics together with the Academy of Finland project will lay good foundations for the candidate to do great science and write high-quality scientific articles leading to a great doctoral dissertation.

An ideal candidate has a Master’s degree in computational or computer science, mathematics, electrical engineering, physics, or a related field. An ideal candidate also has programming skills in languages such as Matlab/Python/C++ and good written and spoken (English) communication skills. Prior knowledge on Bayesian filtering, statistics, probabilistic modeling, signal processing, machine learning, brain imaging, or biomedical applications are considered as advantages. Good grades from undergraduate studies are appreciated. It may also be possible to start as an undergraduate working on the Master’s thesis.

The position is fixed-term and filled initially for 1 year with an option for extension until the end of 4-year PhD studies, jointly funded by the Academy of Finland project and the Department of Electrical Engineering and Automation (EEA).The salary is determined according to the salary system of Aalto University. In addition to the active guidance by Prof. Simo Särkkä there will be at least one post-doc guiding the research along with a few other doctoral candidates. We ensure that you will get all the necessary guidance – but we also give you the freedom to work independently, whatever is the preferred way for you.