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PhD position in the area of Computational Conde ... (No replies)
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The Multiscale Modeling of Materials and Machine Learning Laboratory (M4L Lab: https://www.m4l-lab.com/) at Villanova University seeks one Ph.D. student to work on interdisciplinary research topics that involve Computational Materials Science, First-principles calculations, and Machine Learning. The position starts as early as Fall 2024. Evaluations will begin immediately until the positions are filled.
Topic: First-principles calculations of structural fusion energy materials
The goal of the project is to investigate the thermomechanical response of tungsten-based alloys exposed to fusion-like environments by using first-principles calculations based on density functional theory.
Qualifications:
Starting date: as early as Fall 2024.
How to apply
Interested candidates are invited to email Dr. David Cereceda ([email protected]) with their latest CV, a statement describing their research experience and interests, B.S. and M.S. transcripts, and the contact information for 3 references, all as email attachments in PDF format. This and any other specific inquiries should be addressed with “#Name: Ph.D. applicant-Fall-2024-DFT” in the subject line. Interested candidates are encouraged to submit these materials to Dr. David Cereceda before submitting the online application at Villanova University.
About the Principal Investigator
Dr. David Cereceda is an Assistant Professor in the Department of Mechanical Engineering at Villanova University. Before joining Villanova, Dr. David Cereceda was a Postdoctoral Fellow with Prof. Lori Graham-Brady at Johns Hopkins University, within the Hopkins Extreme Materials Institute. His research at Hopkins is aimed at understanding the dynamic fragmentation of brittle materials under extreme loading conditions. Dr. David Cereceda received his Ph.D. in Nuclear Engineering from Polytechnic University of Madrid in 2015, under the guidance of Prof. Jaime Marian and Prof. José Manuel Perlado. His Ph.D. research, performed at Lawrence Livermore National Laboratory and University of California Los Angeles, was focused on the multiscale modeling of body-centered cubic metals like tungsten from atomistic to engineering scales. His current research focuses on facilitating the discovery, development, and deployment of next-generation structural and bio-inspired materials by creating and validating computational models that leverage physics-based and data-driven techniques. His interdisciplinary research has been recognized with DOE Career Award (2022), and the NSF LEAPS-MPS (2022), among others.
Research website: https://www.m4l-lab.com/