IFP Energies nouvelles (IFPEN) – PhD position in Data Science
A global approach from lab to plant on the use of deep learning and near infrared spectroscopy
IFP Energies nouvelles is a French public-sector research, innovation and training centre. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information see our WEB site. https://www.ifpenergiesnouvelles.com/ifpen/presentation
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefit packages. All PhD students have access to dedicated seminars and training sessions. For more information see our dedicated WEB pages https://www.ifp-school.com/en/programs/phd-theses
Near Infrared spectroscopy (NIR) combined with chemometrics has been used for years in various fields to predict properties, quantify species, classify samples, etc. Today, NIR and chemometrics are under development for biomass characterization and plastic recycling processes. One of the major difficulties is determining a consensus on which spectral preprocessing method and optimal settings to use for the chosen chemometric method (regression, classification, etc.). The common practice is a time-consuming trial-and-error experimentation that must be reiterated when changing the device, for a change in acquisition conditions, etc. In this context, it is necessary to guarantee the prediction quality, and deep learning approaches is the most promising strategy
Duration and start date 3 years, starting 2nd of November 2022.
The use of deep learning on spectroscopic data has been on the rise over the past 5 years. Using a large NIR database, acquired by IFPEN over the last 20 years on different spectrometers, in the laboratory as well as online, the PhD student will develop approaches to facilitate the maintenance and transfer of calibrations from a deep network, without losing performance and knowledge. The relevance of deep learning approaches on spectral data will be assessed on a global approach, from laboratory calibration to transfer to online analysis.
Keywords: Deep Learning, Near Infrared Spectroscopy, Chemometrics, Neural Networks, Transfer Learning.
Academic requirements University master’s degree in data sciences
Language requirements Fluency in English, willingness to learn French
Other requirements Basics in deep learning, attraction for analytical chemistry
Date de début : 02/11/2022
Duration : 3 years
Employer : IFP Energies nouvelles, Lyon, France
Academic supervisor : Maxime MOREAUD, IFPEN, ORCID 0000-0002-4908-401X
Doctoral school : University of Paris-Saclay, STIC, https://www.universite-paris-saclay.fr/en
IFPEN Supervisor : Marion LACOUE-NEGRE, PhD, ORCID 000-0002-1092-2223, email@example.com
please send your cover letter and CV to the IFPEN supervisor indicated here above.