PhD in Interpretable Machine Learning

[Nataliya Sokolovska]

We are looking for a motivated candidate for a PhD program in Artificial Intelligence at the Sorbonne University, Paris.  

The title: Interpretable Models (for Medical Applications): a Marriage between Machine Learning and Decision Theory

If you are interested, please contact (before June, 15): Patrice Perny patric...@lip6.fr; Nataliya Sokolovska nataliya.sokolovska@sorbonne-universite.fr

In this thesis, we would like to take the best of two worlds, machine learning and decision theory, both actively developed in Artificial Intelligence, to propose adaptive and interpretable evaluation models and contribute to produce reliable medical scoring systems which can be integrated into hospital routines.  

An ideal candidate is expected to have a Master 2 in Computer Science (preferably in AI or Mathematics or Operations Research) or an equivalent engineering degree. A background in Machine Learning, optimization, and decision theory or any related field will be appreciated. An ideal candidate will propose, develop, and test numerically the developed methods. It is expected that the candidate provides some theoretical foundations for the methods and also implements them in R/Matlab/Python, and the final product will be publicly available. A candidate is supposed to have an interest in biology/medicine.

Expertise and Supervision. To provide expertise and supervision in decision theory and machine learning and medicine or biology, the PhD thesis will be jointly supervised by Patrice Perny and Nataliya Sokolovska. Moreover, a tight collaboration with clinicians of the NutriOmics team, Pitié-Salpêtrière hospital is planned.

[Nataliya Sokolovska]

We are looking for a motivated candidate for a PhD program in Artificial Intelligence at the Sorbonne University, Paris.  

The title: Interpretable Models (for Medical Applications): a Marriage between Machine Learning and Decision Theory

If you are interested, please contact (before June, 15): Patrice Perny patric...@lip6.fr; Nataliya Sokolovska nataliya.sokolovska@sorbonne-universite.fr

In this thesis, we would like to take the best of two worlds, machine learning and decision theory, to propose adaptive and interpretable evaluation models and contribute to produce reliable medical scoring systems which can be integrated into hospital routines.