Postdoctoral Research Associate - Viswanathan Lab - Mechanical Engineering (MechE)-2015279
In Carnegie Mellon University's Department of Mechanical Engineering (MechE), our faculty members, researchers, and students are revolutionizing focus areas in advanced manufacturing, bioengineering, computational engineering, energy and the environment, product design, and robotics. In addition, they are using their expertise in interdisciplinary research centers across the university.
We are seeking a Postdoctoral Research Associate to join the Venkat Viswanthan Lab at MechE. In this role, you will carry out advanced independent and/or directed research to achieve the objectives of the research project. We will require an in depth knowledge of a niche field, process, or subject area and may involve coordinating and implementing complex research plans, the development of methods of research, testing and data collection, analysis and evaluation, and writing reports which contain descriptive, analytical and evaluative content. You will acquire the professional skills needed to pursue a career path of your choosing.
Our research focus for this Lab is on identifying the scientific principles governing material design, inorganic, organic, and biomaterials, for novel energy conversion and storage routes. The material design is carried out through a suite of computational methods being developed in the group, and validated by experiments. Some key research thrusts we include identifying principles of electrolytes design or organic material that can tune electrode catalysis, identification of new anode, cathode or inorganic materials, and electrolyte materials for next generation batteries, and new electrocatalysts and biomaterials for energy storage and separation applications. In addition, we are involved in several cross-cutting areas such as battery controls, electric vehicle security, and GPU accelerated computing.
Core responsibilities will include:
Machine Learning Driven Potentials to Model Electrode-Electrolyte Interfacial Reactivity and Electrochemical Processes in Batteries and Fuel Cells.
The project will focus on the development of machine learning interatomic potentials for studying interfacial phenomena in electrochemical devices including lithium-ion batteries. While conventional first-principles simulations have provided a good understanding of interfacial reactions, those involving bond breaking and forming require appropriate simulation of interfacial dynamics.
Propose and develop novel machine learning algorithms and methods using frameworks such as COvaRiant MOleculaR Artificial Neural neTworks (Cormorant), Gaussian Approximation Potentials (GAP), Spectral Neighbor Analysis Potentials (SNAP) and Moment Tensor Potentials (MTP), to advance along dimensions of accuracy and computational cost.
Judicious selection and procurement/generation of diverse training and testing data required for the interatomic potential development. The resultant Machine Learning interatomic potential(s) is expected to be widely applicable for solving currently intractable and complicated problems, while providing insights into the decomposition products and ion conduction that works with liquid/solid electrolytes.
Application of core principles of machine learning algorithm development to materials science at the interfaces of Li-ion batteries and fuel cells.
Ph.D. degree in engineering, physics, chemistry, material science, computer science, machine learning, or a related field
Experience in one or more of the following areas: first principles calculations, machine learning, molecular dynamics
Prior experience in the development of machine learning interatomic potentials, high efficiency materials discovery using first-principles calculations, machine learning
Experience with physics-aware machine learning algorithms and performance in the domain of materials science
2 page CV with most relevant details
1 page cover letter stating:
Why you are a good fit by discussing your experience and relevant skills.
What you can uniquely bring to address the outlined challenges/goals.
Why you would like to join our research group as a step towards achieving your broader career goals.
Are you interested in this opportunity with us? Please apply!
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A listing of employee benefits is available at: www.cmu.edu/jobs/benefits-at-a-glance/.
Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.
Job Function: Research
Primary Location: United States-Pennsylvania-Pittsburgh
Time Type: Full Time
Minimum Education Level: Doctorate
Internal Number: 2015279
About Carnegie Mellon University
Carnegie Mellon (www.cmu.edu) is a private, internationally ranked research university with programs in areas ranging from science, technology and business, to public policy, the humanities and the arts. More than 12,000 students in the university’s seven schools and colleges benefit from a small student-to-faculty ratio and an education characterized by its focus on creating and implementing solutions for real problems, interdisciplinary collaboration and innovation. A global university, Carnegie Mellon’s main campus in the United States is in Pittsburgh, Pa. It has campuses in California’s Silicon Valley and Qatar, and programs in Africa, Asia, Australia, Europe and Mexico.