Connectionists: PhD position on Explainable ML in Health Science

[Georgios Exarchakis]

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*PhD position on Explainable ML in Health Science*

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Keywords: Scattering Transforms, Convolutional Neural Networks, Health
Sciences

Profile: M.Sc. Computer Science/Mathematics/Physics or a related field

Institute: IHU Strasbourg/University of Strasbourg

Location: Strasbourg, France

Supervisor: Dr Georgios Exarchakis


We offer a PhD position attached to the CAMMA team
<http://camma.u-strasbg.fr/>at IHU Strasbourg/University of Strasbourg
for a motivated student interested in contributing to the development of
Machine Learning algorithms for use in Health Sciences. The Institute
provides a multi-disciplinary environment where clinicians collaborate
with engineers to provide real-world surgical solutions. 

The PhD position focuses on explainable approaches to AI with
applications to Health Science. We consider approaches with foundations
in signal processing and machine learning, such as the Wavelet
Scattering Transforms[1,2] and Deep Convolutional Neural Networks.
However, the candidate is expected to introduce novel model
architectures for applications on disease diagnosis from CT and MRI
scans. The goal of the project is to produce accurate predictive
algorithms with performance guarantees that can be established analytically.

An ideal applicant will have a strong background in mathematics or
physics preferably with a specialisation in machine learning, and signal
processing. Strong analytical skills will be required for the successful
participation in the project. Familiarity with Machine Learning
libraries and The project also requires the ability to develop
Convolutional Neural Networks in the Python programming language with at
least one of the popular ML libraries, e.g. Pytorch, TensorFlow.
Proficiency in spoken and written English is mandatory.


Contact Dr Georgios Exarchakis <https://exarchakis.net/>at
georgios.exarchakis at ihu-strasbourg.eufor applications and further
inquiries. An application should include a one page motivation for
letter, a CV, copies of earlier degrees and transcripts. To facilitate
processing of applications please use the title “PhD on Explainable AI”.


[1] Eickenberg M., Exarchakis G., Hirn M., Mallat S., Thiry L. (2018).
Solid harmonic wavelet scattering for predictions of molecule
properties.The Journal of chemical physics, 2018.

[2] Andreux M., Angles T., Exarchakis G., Leonarduzzi R., Rochette G.,
Thiry L., Zarka J., Mallat S., Andén J., Belilovsky E., Bruna J.,
Lostanlen V., Chaudhary M., Hirn M. J., Oyallon E., Zhang S., Cella C.,
Eickenberg M. (2020). Kymatio: Scattering Transforms in Python.Journal
of Machine Learning Research, 2020.

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