This 4-year position is organised as a joint PhD between KU Leuven campus Kulak (Kortrijk, Belgium) and Leiden University Medical Centre (the Netherlands). On the KU Leuven side, the work will be mainly performed in the applied math research group of Prof. Dr. Hans Dierckx, who is using methods from mathematical physics to unravel the spatio-temporal mechanisms behind heart rhythm disorders (Department of Mathematics, KU Leuven). Clinical data will be provided by KU Leuven University Hospital (Gasthuisberg Hospital, Leuven). In Leiden, the research will be performed in the Laboratory of Experimental Cardiology of ERC-grantee Dr. Daniël Pijnappels at Leiden University (the Netherlands). Here scalable human culture models of heart rhythm disorders will be studied, which data will be analysed and used for modelling purposes. See also https://hartlongcentrum.nl/research/laboratory-of-experimental-cardiology/
Heart rhythm disorders are one of the major causes of death globally, claiming about 16 million lives annually. Although different rhythm disorders can be classified based on electrical recordings, the precise temporal organisation of the electrical activity in the heart during arrhythmias remains incompletely understood.
Over the past decades, detailed mathematical models of coupled non-linear partial differential equations have been developed, and these are being solved on supercomputers in order to forecast the evolution of an arrhythmia. However, the “virtual hearts” that are nowadays created for individual patients to predict outcome of surgery or therapy still have limited predictive power.
In this project, we will use machine learning to train our “virtual cells” to behave as cardiac cells, learning from large optical datasets collected in the Pijnappels lab. In the second phase of the project, this process is repeated with learning from electrical data from patients, in order to create a patient-specific predictive computer model of cardiac activity.
Candidates must hold a recent Master’s degree in Mathematics, Physics, Biophysics, Artificial Intelligence, or Engineering in a relevant subdiscipline (e.g. applied physics, biomedical, mathematical techniques in engineering, …). Practical experience with machine learning will be regarded as an asset for the position.
Candidates should be motivated, independent, critically thinking and should have a strong team-player skill. Excellent proficiency in Dutch and/or English is required, as well as good communication skills, both oral and written. They can also be asked to contribute to the teaching activities at Kortrijk, to Bachelor students in Mathematics, Physics and Engineering.
Motivation letters are free in format but should clearly mention why you are interested in this particular position and demonstrate that you satisfy the listed requirements.
For more information please contact Prof. dr. Hans Dierckx (KU Leuven) via email@example.com or Dr. Daniël Pijnappels (Leiden University) via D.A.Pijnappels@lumc.nl
Presenting yourself as a candidate for the position requires uploading a CV (with obtained grades) and motivation letter via the link below. Earlier applications are encouraged and will be considered as soon as they are received. Interviews will take place via video-conferencing. The expected starting date for the position is October-December 2020.
You can apply for this job no later than August 13, 2020 via the online application tool
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.Läs mer
|Titel||PhD Researcher on Machine Learning Models for Electrical Excitation of the Heart|
|Job location||Oude Markt 13, 3000 Leuven|
|Publicerad||juli 14, 2020|
|Sista ansökningsdatum||augusti 13, 2020|
|Ämnen||Biomedicinsk teknik,   Biomedicin,   Biofysik,   Kardiologi,   Artificiell intelligens,   Databehandling inom matematik, naturvetenskap, teknik och medicin,   Matematisk fysik,   Medicinsk fysik,   Tillämpad fysik,    and 1 more. Maskininlärning  |