Arun Mannodi Kanakkithodi

Purdue University’s School of Materials Engineering is advertising a postdoctoral researcher opening in computational materials design and materials informatics in the group of Arun Mannodi Kanakkithodi (Assistant Professor). The research direction includes ab initio simulations , density functional theory , defect simulations , machine learning interatomic potentials , and other modern ML approaches for materials discovery and prediction. The flyer highlights topics such as first-principles simulations of electronic, optical, and defect properties; designing defect-tolerant and dopable wide-band-gap semiconductors; developing ML interatomic potentials for defects in solids; and predicting synthesizability and synthesis routes of materials. This opportunity is based at Purdue University in West Lafayette, Indiana, USA . The appointment is for one year and may be renewed depending on funding and performance. Due to funding restrictions, the position is only open to US citizens and permanent residents . Applicants should have a PhD in Materials Science and Engineering, Physics, Chemistry, or a related field, along with strong expertise in solid-state physics, crystallography, thermodynamics of materials, band structure theory, electronic and optical properties, defect physics, VASP-based DFT, Unix/Python/LaTeX, programming, machine learning algorithms, and scientific communication. To apply, send a single PDF to [email protected] with a 1-page cover letter, CV (max 4 pages), and the names, emails, and phone numbers of at least three references. Applications are reviewed immediately and will continue until the position is filled.

Postdoctoral Researcher opening in computational materials science at Purdue University , in the research group of Prof. Arun Mannodi Kanakkithodi (School of Materials Engineering). The project focuses on ab initio simulations , density functional theory (DFT) , computational materials design , materials informatics , and machine learning for materials research. Specific topics include electronic, optical, and defect properties of semiconductors, defect-tolerant and dopable wide-bandgap materials, and prediction of synthesizability and synthesis routes. The group also works on high-throughput DFT, descriptor-based regression models, crystal graph neural networks, and open computational datasets. Eligible applicants should hold a PhD in Materials Science and Engineering, Physics, Chemistry, or a related field . Strong experience with VASP , solid-state physics, Unix, LaTeX, Python, and programming is expected. Experience with machine learning algorithms, training machine-learning interatomic potentials, and foundation models is desirable, as is familiarity with large language models and strong scientific communication skills. This is a postdoctoral position, based on the West Lafayette, Indiana, USA campus. The appointment is for one year and may be renewed depending on funding and performance. The post is restricted to US citizens or permanent residents because of funding restrictions. How to apply: email a single PDF with a 1-page cover letter, CV (max 4 pages), and names, emails, and phone numbers of at least three references to [email protected] . Applications are reviewed immediately and accepted until the position is filled.

Postdoctoral researcher opening in computational materials design and materials informatics at Purdue University (School of Materials Engineering). The group led by Arun Mannodi Kanakkithodi is seeking a postdoc with strong experience in density functional theory , defect simulations , machine learning interatomic potentials , and related modern machine learning methods for materials research. This is a research-focused opportunity in advanced materials science with emphasis on computational modeling, atomistic simulation, and data-driven approaches to materials discovery/design. The post explicitly notes that, because of funding restrictions, the position is open only to US citizens and permanent residents . No deadline, stipend amount, or full application portal is provided in the text. The post directs interested candidates to the attached flyer for more information and to the professor’s LinkedIn post/profile for follow-up.