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Urgent! Machine-learning approaches for nanoparticle simulations Position in Lille - UMET-CNRS
Description
Research overview While nanocrystals in material science are ubiquitous, the mechanisms of their formation which spans from nucleation to crystal growth remain one of the most intriguing process in nature.
From the fundamental point of view, the main challenge is related to the stochastic nature of the process and the very small number of atoms involved.
Altogether, it leads to numerous technical difficulties that have hindered the ability to systemically study nucleation in the most complex systems.
In this context, numerical simulations involving both statistical mechanics and molecular quantum mechanics have been pivotal for providing an atomistic view of the underlying processes.
Recently, machine-learning approaches have enabled for more precise and more extensive use of these numerical simulations which are now increasingly converging with experimental measurements.
Project The student will have the opportunity to pursue numerous research avenues depending on their preferences.
Indeed, further numerical developments based on machine-learning approaches can be envisaged for statistically sampling free energy barriers associated to nucleation or for better modeling the interactions between atoms fitted on electronic structure calculations.
Meanwhile, it will also be possible to focus on a specific material and study different approaches leading to the nanocrystal formation including gas phase condensation, solvent mediated synthesis or deposition mechanisms.
Organization The internship will be carried out at the Unité Matériaux et Transformations which is located at the Université de Lille.
The student will benefit from the supervision of Julien Lam who is a CNRS researcher expert in numerical simulations for atomistic simulations.
Potential students are not required prior knowledge of computational materials science as they will be trained in a large number of research domains including molecular quantum mechanics, statistical physics, machine-learning, material science and computer programming.
Profile
- Master in physics, chemistry or materials science
- Good knowledge of statistical mechanics and computational physics/chemistry
- Previous experience in C++/Python/Bash would also be a plus
Starting date
Dès que possible✨ Smart • Intelligent • Private • Secure
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Real-time Machine learning Jobs Trends in Lille, France (Graphical Representation)
Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph below. This graph displays the job market trends for Machine learning in Lille, France using a bar chart to represent the number of jobs available and a trend line to illustrate the trend over time. Specifically, the graph shows 1301 jobs in France and 6 jobs in Lille. This comprehensive analysis highlights market share and opportunities for professionals in Machine learning roles. These dynamic trends provide a better understanding of the job market landscape in these regions.
Great news! UMET-CNRS is currently hiring and seeking a Machine learning approaches for nanoparticle simulations to join their team. Feel free to download the job details.
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An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at UMET-CNRS adheres to the cultural norms as outlined by Expertini.
The fundamental ethical values are:The average salary range for a Machine learning approaches for nanoparticle simulations Jobs France varies, but the pay scale is rated "Standard" in Lille. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.
Key qualifications for Machine learning approaches for nanoparticle simulations typically include Physical Scientists and a list of qualifications and expertise as mentioned in the job specification. Be sure to check the specific job listing for detailed requirements and qualifications.
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