France-Stanford Center - Undergraduate Internship CentraleSupélec Process Engineering
Sponsored by
Stanford Global Studies
France-Stanford Center for Interdisciplinary Studies
Funding Type:
Stipend
Open To:
Freshman
Sophomore
Junior
Senior
Summer
Applications closed
Applications closed on February 5, 2020
Approximate Offer Date:
Sunday, March 15, 2020
CentraleSupélec is an internationally renowned Higher Education and Research Institution. Its excellence lies in its combination of fundamental and applied sciences for innovation with societal impact. The Process Engineering and Materials Laboratory (LGPM) is involved in two inextricably linked fields of investigation: Process Engineering and Materials. Modeling, simulation and experimentation are the key words in common between these different research subjects. This complementarity allows an understanding of microscopic phenomena to be used in the simulation, optimization, and intensification of transformation and development processes. Our expertise is, in particular, applied to sustainable aspects of processes (e.g., material and energy savings) and bio-processes (use of renewable resources). These fields have been strengthened with the introduction of a “white biotechnologies” center in 2010, which allows the laboratory to be present in the promising field of bio-economy.
Eligibility and Requirements:
Internship in Microbial Growth Image Analysis. The Process Engineering and Materials Laboratory of Ecole CentraleSupélec conducts research on the analysis of microorganism growth. In recent years the LGPM has invested heavily in its imaging capacity with the acquisition of a confocal microscope, an axio-binocular microscope, x-ray tomography, and a scanning electron microscope. This investment has been accompanied by an expansion in the modeling capability of the Department. Despite great successes a problem that was quickly encountered is that of data treatment, which remains largely manual. The proposed internship is focused on data analysis and programming. During this internship, the student will work with a research engineer and expert in microscopy under the supervision of Professor Behnam Taidi. The student will be particularly involved in the analysis of microbial growth performed using image processing. It will involve modeling, including machine learning to predict the chances of growth of individual Chlorella vulgaris microalgae cells. Treatment of images obtained by confocal microscopy. The work will also involve treatment of images obtained by confocal microscopy, followed by code development and, if necessary, machine learning to determine a way to predict the chances of growth of individual cell in a population from images obtained by microscopy. A first stage has provided promising results that upon completion could lead to a scientific publication.