Organisation/Company: CNRS
Department: Laboratoire de réactivité et chimie des solides
Research Field: Chemistry » Physical chemistry, Chemistry » Computational chemistry
Researcher Profile: First Stage Researcher (R1)
Country: France
Application Deadline: 27 Nov 2024 - 23:59 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 35
Offer Starting Date: 1 Feb 2025
Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure? No
Offer Description The recruited researcher will have the opportunity to work as part of an international, interdisciplinary team of 17 doctoral candidates, based at universities and industrial firms throughout Europe. She/he will be supported by two mentors within the PREDICTOR project, and will have multiple opportunities to participate in professional and personal development training. Through her/his work she/he will gain a unique skill-set at the interface between modelling and simulation, high-throughput experimentation/characterization, and self-optimization and data management over different length scales from nano to the macroscopic level.
She/he is expected to finish the project with a PhD thesis and to disseminate the results through patents (if applicable), publications in peer-reviewed journals, and presentations at international conferences.
At LRCS and in Prof. Franco's team, the PhD candidate will find a scientific environment of excellence, with state-of-the-art equipment and computational facilities, within a highly international and friendly atmosphere. Twice a year, the laboratory organizes the Scientific Days, an event where all the students and staff of the lab present their research activities, followed by barbecues and get-togethers.
Qualifications/Experience - In accordance with the European Union's funding rules for doctoral networks, applicants must NOT yet have a PhD.
- The PhD candidate should have a strong background in Artificial Intelligence (AI) methods, including machine learning, and data science methods as a whole. Experience with Python programming language and classical AI libraries is necessary. The candidate should also have a background in computational modeling (e.g. Computational Fluid Dynamics, Lattice Boltzmann Method) and knowledge of the physical chemistry of electrolytes. Knowledge and/or experience in the field of batteries or redox flow batteries, as well as in the electrolyte preparation methods for these technologies, will also be appreciated. The PhD candidate should be open-minded, highly motivated, dynamic, and possess an excellent level of English both spoken and written.
Mobility The applicant must not have resided or carried out her/his main activity (work, studies, etc.) in France for more than 12 months in the past 3 years. The PhD candidate will visit Fraunhofer ICT (Prof. Dr. Jens Noack's lab) for 3 months to work on electrolyte measurements.
Project Details The advertised subproject is fully funded by the Marie Sklodowska-Curie European Training Network "PREDICTOR". It will be carried out by one doctoral candidate at the Laboratoire de Réactivité de Chimie des Solides -LRCS- (CNRS & Université de Picardie Jules Verne joint laboratory), in Amiens, France under the supervision of Prof. Alejandro A. Franco over a period of 36 months. LRCS is constituted of 140 researchers working in the field of batteries, with approximately 30 nationalities represented. Prof. Franco's team is located at LRCS, and has a recognized research activity in the field of battery modeling and digitalization.
The PhD candidate will be in charge of:
The development of innovative data fusion algorithms combining information arising from the multiple characterization techniques used in PREDICTOR (e.g. conductivity cells, UV/VIS spectroscopy, RAMAN spectroscopy, electrochemical impedance spectroscopy, cyclic voltammetry) and electrolyte processing physical models that she/he will develop.
The development and demonstration of machine learning models using this fused data for self-optimizing the electrolyte formulations in regards to target properties (e.g. conductivity and viscosity).
The PhD candidate will work with advanced Artificial Intelligence (AI)/Machine Learning methodologies, by using Python programming language and classical related AI libraries. The candidate will also perform computer (numerical) simulations solving physical models describing the electrolyte manufacturing process. Such simulations can combine Computational Fluid Dynamics and elementary kinetic approaches.
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