Mots-Clés

Artificial Intelligence, Probabilistic Graphical Event Model, Ontology, Machine Learning, Surgical Process Modelling.

Description

Context
DUKe (Data User Knowledge) research group at LS2N, UMR CNRS 6004, is one of the laboratory's main teams in "Data and Decision Science" field, with its skills in data manipulation, data mining and interaction.
Within this framework, the research group has, among other things, developed numerous algorithms for learning and manipulating probabilistic graphical models (Bayesian networks, dynamic Bayesian networks, relational Bayesian networks, graphical event models) gathered within the PILGRIM C++ software library.
This PhD thesis is part of the SPARS project (Sequential Pattern Analysis in Robotic Surgery: Understanding Surgery), funded by Labex CominLabs, in collaboration with LTSI/INSERM/Université Rennes 1 and INRIA.

The objective of this project is to propose data analysis methods to better understand complex technical human activities, such as surgery. Surgery is a complex activity, that depends on many factors, including the patient and surgeon characteristics. Such complexity and variability explain why there is almost no detailed study of the surgical practice yet. Until now, the surgical procedure performed in the operating room is considered as a whole, as a black-box and is technically described with few words. Analysis usually consisted in comparing impact of different surgical approaches or of different pre-operative clinical patient’s parameters on post-operative outcomes. In the SPARS project, we will rely on a combination of data and model-driven approaches to analyze and compare kinematics of whole surgical procedures acquired during robotic assisted hysterectomies.

Funding:
The PhD fellowship is fully funded for 3 years from september-October 2022.

Profile of the candidate:
The candidate should have a master's degree in computer science or equivalent, as well as knowledge of machine learning, probabilistic graphical models and knowledge representation. Good skills in machine
learning is mandatory. Some knowledge in knowledge representation will be a plus.
The programming environment associated with this project also requires some knowledge of C++ programming language.
The personal qualities expected are mainly autonomy and a taste for interdisciplinary work, rigour and abstraction, as well as writing skills (in French and English).