danialfaghihi's blog

Two fully supported Ph.D. positions are open in Predictive Computational Engineering (PCE) Research Lab at the University at Buffalo for research in advanced computational modeling and algorithms, scientific machine learning, and uncertainty quantification.

Research Areas: 

Position 1: Image-Guided Personalized Radiotherapy Optimization for Tumor Growth

Position 2: Integrated Physics-Based and Machine Learning Models for Material Design

Jessica  Zhang, Alireza Tabarraei, Kathryn Maupin, and myself are organizing a mini-symposium on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics,” in the ASME IMECE 2023 (October 29 – November 2, 2023). The symposium solicits abstracts on novel scientific machine learning (SciML) and uncertainty quantification (UQ) methods a wide range of applications across science, engineering, and medicine.

An immediate Ph.D. position is available in the Predictive Computational Engineering (PCE) Lab in the Department of Mechanical and Aerospace Engineering. The research project aims at developing novel computational methods for predictive modeling and uncertainty quantification of biomimetic-inspired materials systems. Candidate should already be in the U.S. and must have a Master's degree in engineering or applied math. A strong background in computational and applied mechanics is desired.

Our paper on the phase-field mixture theory of tumor growth is published in JMPS. The continuum model simulates significant mechano-chemo-biological features of avascular tumor growth in the various microenvironment, i.e., nutrient concentration and mechanical stress.

Faghihi, Feng, Lima, Oden, and Yankeelov (2020). A Coupled Mass Transport and Deformation Theory of Multi-constituent Tumor Growth. Journal of the Mechanics and Physics of Solids, 103936.

Dear Colleagues,

As part of the IMECE 2020 (November 13-19, 2020, Portland, Oregon), we are organizing a topic on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics.” It is listed in Track 12: Mechanics of Solids, Structures, and Fluids: https://event.asme.org/IMECE/Program/Tracks-Topics.

Multiple Ph.D. students are being sought to fill openings in the Predictive Computational Engineering (PCE) Lab in the Department of Mechanical and Aerospace Engineering. PCE Lab concerns with multidisciplinary research at the intersect of multiscale modeling of materials, physics-based machine learning, and scientific computing.