Sobre esta vaga de Accelerated Science Intern, Bridging Atomistic Modeling to Real Electrochemical Systems na Tri
At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.
This is a paid 12-week internship opportunity and is a hybrid, in-office role.
Here’s a glimpse into the Internship experience from some of our TRI interns!
The Team
The long-term vision of TRI’s Accelerated Materials Design and Discovery (AMDD) program is to accelerate the development of truly emissions-free mobility. Realizing this vision will require the discovery of new materials and devices for batteries, fuel cells, and more. Our aim at TRI is to merge sophisticated computational materials modeling, new experimental data, artificial intelligence, and automation to significantly accelerate materials research in this area. Our focus is on developing tools and capabilities to enable this acceleration. We collaborate closely with a dozen universities and national labs and our colleagues across global Toyota. AMDD seeks to develop and translate the newest technologies into practice, both within Toyota and the open research community, more broadly.
This Internship
We are looking for an intern researcher to contribute to the development of atomistic catalysis modeling that bridges the gap between current simulation (e.g. DFT) and in-situ catalysis experiments. We will develop reasoning models with embedded chemical and atomistic descriptors as well as experimental parameters and measurements. Your day-to-day may include: literature review of computational methods and model construction and validation. We welcome you to join a unique team of scientists and engineers where you will constantly learn new skills at the interface of materials science and thoughtful
Responsibilities
Research multiscale physics phenomena required in electrocatalysis modeling.
Data analytics for experimental electrocatalyst design and testing.
Identify key materials understanding that enable translation of existing models to new materials.
Develop new reasoning models incorporating atomistic modeling and experiments.
Collaborate with the team on automated reasoning methods.
Present findings within the team and to the greater group.
Qualifications
Are currently enrolled in a doctoral program in STEM subjects (chemistry, chemical engineering, materials science, electrical engineering, mechanical engineering, or a related discipline).
Hands-on experience with advanced 3-electrode cell catalyst electrochemical evaluation.
Experience with standard material characterization techniques (x-ray diffraction, scanning electron microscopy, etc.).
Published manuscripts in the electrocatalysis field.
Bonus Qualifications
Experience coding in Python.
Microkinetic modeling experience.
Experience in DFT simulations.
Experience in in-situ electrocatalysis characterization.
