Computational Mechanics & Artificial Intelligence Lab
Nick Napoleon Vlassis is an Assistant Professor in the Department of Mechanical & Aerospace Engineering at Rutgers University.
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Geometric Deep Learning for Computational Mechanics Part I: Anisotropic Hyperelasticity
Geometric Deep Learning for Computational Mechanics Part II: Graph Embedding for Interpretable Multiscale Plasticity
Sobolev Training of Thermodynamic-Informed Neural Networks for Interpretable Elasto-Plasticity Models with Level Set Hardening
Molecular Dynamics Inferred Transfer Learning Models for Finite-Strain Hyperelasticity of Monoclinic Crystals: Sobolev Training and Validations Against Physical Constraints
Component-Based Machine Learning Paradigm for Discovering Rate-Dependent and Pressure-Sensitive Level-Set Plasticity Models
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
nick.vlassis@rutgers.edu
Hiring Opportunities
Multiple PhD Positions Available in Computational Mechanics – Spring Semester 2024 - Fall Semester 2025
Applications are currently being accepted for multiple fully-funded PhD positions in the Department of Mechanical & Aerospace Engineering at Rutgers University starting in the spring semester of 2024 or fall semester of 2025. Looking for highly motivated and dedicated individuals who wish to pursue a career in academic research.
Key Responsibilities:
Successful candidates will be involved in high-impact research in the field of computational mechanics and machine learning. Your tasks will involve theoretical development, numerical implementations, and simulations, with the objective of advancing the state-of-the-art in the discipline.
Minimum Qualifications:
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Master’s degree in Mechanical Engineering, Aerospace Engineering, Civil Engineering, Applied Mechanics, Applied Mathematics, or any other related discipline.
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Strong background in computational mechanics and proficient mathematical skills, including linear algebra, undergraduate calculus, and ordinary/partial differential equations.
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Proficiency in Python and C++ programming languages. Candidates should demonstrate an ability to convert their theoretical ideas into program code.
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Excellent communication skills. It is vital that candidates are able to efficiently express their research outcomes, and engage effectively with academic staff, fellow students, and the wider research community.
To apply, please submit a CV detailing your qualifications, experiences, and any relevant projects or research you have been a part of. Please send your application and any enquiries directly to nick.vlassis@rutgers.edu