Ph.D. in Machine Learning Fairness applied to Medical Images W/M
Subsidiary : Biosignal Processing
Contract type: Fixed-Term contract
Work time: Full time
Idiap is an independent, not-for-profit, research institute
accredited and funded by the Swiss Federal Government, the State of
Valais, and the City of Martigny.
Idiap offers competitive salaries and working conditions at all levels in a dynamic, multicultural environment. Idiap is an equal opportunity employer. We specifically encourage women and minorities to apply.
Idiap is located in the town of Martigny in Valais, Switzerland, offering exceptional quality of life, exciting recreational activities, including hiking, climbing and skiing, as well as varied cultural activities. It is within close proximity to Lausanne and Geneva. Although Idiap is located in the French part of Switzerland, English is the official working language.
For frequently asked questions (FAQs) about living in Switzerland, please go to https://www.idiap.ch/en/faq
The Biosignal Processing Group (https://www.idiap.ch/en/scient...) at the Idiap Research Institute (https://www.idiap.ch) invites applications for one Ph.D. student to work on the recently granted SNSF Project FairMI - "Machine Learning Fairness with Application to Medical Images". The project will be developed in the framework of a collaboration between Idiap with Dr. Andre Anjos (https://anjos.ai), the Federal University of São Paulo with Prof. Lilian Berton (https://orcid.org/0000-0003-13...), the Medical School of São Paulo University (Prof. Marcelo Tatit Sapienza, Prof. Carlos Alberto Buchpiguel (https://orcid.org/0000-0003-09...), and the Medical School of the Federal University of Rio de Janeiro (Dr. Anete Trajman (https://orcid.org/0000-0002-40...)).
Context: Algorithmic bias remains one of the key challenges for the wider applicability of Machine Learning (ML) in healthcare. Statistical modeling of natural phenomena has gained traction due to increased representation capacity and data availability. In medicine, particularly, the use of ML models has increased significantly in recent years, especially to support large scale screening, and diagnosis. However impactful, the study of demographic bias of newly developed or already deployed ML solutions in this domain remains largely unaddressed. This is particularly true in the medical imaging domain, where it remains challenging to associate demographic attributes with features.
In this project, the student will contribute in advancing the state of the art in Machine Learning fairness applied to the design of computer-aided diagnosis systems by e.g., designing new classification approaches, redefining and quantifying operational boundaries, and generating complementary synthetic data. Appointments as a PhD student are typically for 4 years, conditional to successful progress, and should lead to a PhD dissertation, granted by the "Ecole Polytechnique Fédérale de Lausanne (EPFL)". Working at Idiap in Martigny, this candidate will become a doctoral student at EPFL, and thus also has to be accepted for enrolment there before joining Idiap.
We are looking for profiles that have:
* a masters degree (or equivalent) in engineering, physics, applied mathematics or similar.
* a solid background in machine learning, computer vision, programming (at least Python, and shell scripting languages for Linux), open scientific principles, reproducibility, analytical and writing skills.
* a strong sense of collaboration and collaborative work.
To balance the group, we especially encourage applicants from traditionally underrepresented groups, however all applications will be judged on merit.
Shortlisted candidate may undergo a test including technical reading and writing in English, and programming (in Python). To avoid discrimination, please do not mention your gender, age and marital status on your CV.
In order to avoid discrimination, please do not mention your gender, age and marital status on your CV.
- English - Level advanced
- French - Level beginner