danialfaghihi's blog
https://imechanica.org/blog/10106025
enTwo Funded Ph.D. Positions at the University at Buffalo: Scientific Machine Learning and Predictive Modeling in Materials and Tumor Growth
https://imechanica.org/node/26667
<div class="field field-name-taxonomy-vocabulary-6 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/73">job</a></div></div></div><div class="field field-name-taxonomy-vocabulary-8 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/13794"># Finite Element modeling; #machinelearning; #computational mechanics; #uncertainty quantification</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p class="MsoNormal">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.</p>
<p class="MsoNormal"><strong>Research Areas:</strong> </p>
<p class="MsoNormal">Position 1: Image-Guided Personalized Radiotherapy Optimization for Tumor Growth</p>
<p class="MsoNormal">Position 2: Integrated Physics-Based and Machine Learning Models for Material Design</p>
<p class="MsoNormal"><strong>Requirements:</strong></p>
<p class="MsoListParagraphCxSpFirst">· Master's degree in Mechanical Engineering, Civil Engineering, or Applied and Computational Mathematics. </p>
<p class="MsoListParagraphCxSpMiddle">· Proficiency in Python and C++. </p>
<p class="MsoListParagraphCxSpLast">· Knowledge in scientific machine learning, finite element methods, Bayesian inference, and optimization is a plus.</p>
<p class="MsoNormal"><strong>Applications:</strong></p>
<p class="MsoNormal">These positions will be filled in either Fall 2023 or Spring 2024.</p>
<p class="MsoNormal">Submit your cover letter, CV, and publications (if applicable) to Dr. Danial Faghihi at <a href="mailto:danialfa@buffalo.edu" target="_new">danialfa@buffalo.edu</a> with the subject line "Ph.D. Application – [Name]". Evaluation of candidates begins immediately.</p>
</div></div></div>Fri, 19 May 2023 17:33:40 +0000danialfaghihi26667 at https://imechanica.orghttps://imechanica.org/node/26667#commentshttps://imechanica.org/crss/node/26667Mini-symposium at ASME IMECE 2023 on scientific machine learning and uncertainty quantification
https://imechanica.org/node/26539
<div class="field field-name-taxonomy-vocabulary-6 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/74">conference</a></div></div></div><div class="field field-name-taxonomy-vocabulary-8 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/13744">machine learning; optimization; uncertainty quantification; predictive modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p class="MsoNoSpacing">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. Please consider submitting an abstract to this topic before March 6, 2023 and at <a href="https://event.asme.org/IMECE">https://event.asme.org/IMECE</a></p>
</div></div></div>Fri, 24 Feb 2023 16:59:43 +0000danialfaghihi26539 at https://imechanica.orghttps://imechanica.org/node/26539#commentshttps://imechanica.org/crss/node/26539Postdoc position on Computational Material Engineering at University at Buffalo
https://imechanica.org/node/25760
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</div></div></div>Mon, 07 Feb 2022 22:52:47 +0000danialfaghihi25760 at https://imechanica.orghttps://imechanica.org/node/25760#commentshttps://imechanica.org/crss/node/25760Immediate PhD Position in Computational Engineering at the University at Buffalo for Fall/Spring 2020
https://imechanica.org/node/24324
<div class="field field-name-taxonomy-vocabulary-6 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/73">job</a></div></div></div><div class="field field-name-taxonomy-vocabulary-8 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/12883">PhD position; # Finite Element modeling; Multiscale modeling; Uncertainty Quantification</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p class="MsoNormal"><span>An immediate Ph.D. position is available in the Predictive Computational Engineering (PCE) Lab in the Department of Mechanical and Aerospace Engineering. </span><span>The research project aims at developing novel computational methods for predictive modeling and uncertainty quantification of biomimetic-inspired materials systems. </span><strong><span>Candidate should already be in the U.S. and must have a Master's degree in engineering or applied math.</span></strong><span> A strong background in computational and applied mechanics is desired. If interested, contact Dr. Faghihi directly at <a href="mailto:danialfa@buffalo.edu">danialfa@buffalo.edu</a>. Please include a CV along with a brief description of prior research experiences.</span></p>
</div></div></div>Thu, 25 Jun 2020 14:02:00 +0000danialfaghihi24324 at https://imechanica.orghttps://imechanica.org/node/24324#commentshttps://imechanica.org/crss/node/24324A phase-field mixture theory of tumor growth
https://imechanica.org/node/24085
<div class="field field-name-taxonomy-vocabulary-6 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/76">research</a></div></div></div><div class="field field-name-taxonomy-vocabulary-8 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/2375">phase-field modeling</a></div><div class="field-item odd"><a href="/taxonomy/term/8380">hyper elasticity</a></div><div class="field-item even"><a href="/taxonomy/term/12533">cancer biophysics</a></div><div class="field-item odd"><a href="/taxonomy/term/9229">biomechancis</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p class="MsoNormal">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.</p>
<p class="MsoNormal"><span>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.</span></p>
<p class="MsoNormal"><span><a title="link to jmps" href="https://www.sciencedirect.com/science/article/pii/S0022509620301721" target="_blank" rel="noopener noreferrer">https://www.sciencedirect.com/science/article/pii/S0022509620301721</a></span> </p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"><span><strong>Abstract:</strong></span></p>
<p class="MsoNormal"><span>We develop a general class of thermodynamically consistent, continuum models based on mixture theory with phase effects that describe the behavior of a mass of multiple inter- acting constituents. The constituents consist of solid species undergoing large elastic de- formations and incompressible viscous fluids. The fundamental building blocks framing the mixture theories consist of the mass balance law of diffusing species and microscopic (cellular scale) and macroscopic (tissue scale) force balances, as well as energy balance and the entropy production inequality derived from the first and second laws of thermodynamics. A general phase-field framework is developed by closing the system through postulating constitutive equations (i.e., specific forms of free energy and rate of dissipation potentials) to depict the growth of tumors in a microenvironment. A notable feature of this theory is that it contains a unified continuum mechanics framework for addressing the interactions of multiple species evolving in both space and time and involved in biological growth of soft tissues (e.g., tumor cells and nutrients). The formulation also accounts for the regulating roles of the mechanical deformation on the growth of tumors, through a physically and mathematically consistent coupled diffusion and deformation framework. A new algorithm for numerical approximation of the proposed model using mixed finite elements is presented. The results of numerical experiments indicate that the proposed theory captures critical features of avascular tumor growth in the various microenvironment of living tissue, in agreement with the experimental studies in the literature.</span></p>
</div></div></div>Sat, 04 Apr 2020 17:41:28 +0000danialfaghihi24085 at https://imechanica.orghttps://imechanica.org/node/24085#commentshttps://imechanica.org/crss/node/24085Mini-symposium on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics” at IMECE 2020 conference
https://imechanica.org/node/24063
<div class="field field-name-taxonomy-vocabulary-6 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/74">conference</a></div></div></div><div class="field field-name-taxonomy-vocabulary-8 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/162">computational mechanics</a></div><div class="field-item odd"><a href="/taxonomy/term/10902">machine learning</a></div><div class="field-item even"><a href="/taxonomy/term/10901">predictive modeling</a></div><div class="field-item odd"><a href="/taxonomy/term/947">uncertainty quantification</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p><span>Dear Colleagues,</span></p>
<p><span> </span></p>
<p><span><span>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 <em>Track 12: Mechanics of Solids, Structures, and Fluids: </em></span><a href="https://event.asme.org/IMECE/Program/Tracks-Topics"><span>https://event.asme.org/IMECE/Program/Tracks-Topics</span></a><em><span>.</span></em></span></p>
<p><span> </span></p>
<p><span>You are cordially invited to submit an abstract to this topic. This mini-symposium focuses on the recent developments of novel machine learning methods and predictive physics-based computational models. The topics of interest include but not limited to data science, model validation, uncertainty quantification, real-time assimilation of data, reduced-order modeling, optimal design of experiments, Bayesian inference, as well as design, control, and decision making under uncertainty.</span></p>
<p><span> </span></p>
<p><span><span><span>The presentation only abstract submission deadline is <strong>July 13, 2020</strong></span></span><span>. Abstracts must be submitted online at </span><a href="https://event.asme.org/IMECE" target="_blank" rel="noopener noreferrer"><span>https://event.asme.org/IMECE</span></a><span>.</span></span></p>
<p><span> </span></p>
<p><span>We hope that you’ll be able to attend and present your work at the conference.</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Best regards</span></p>
<p class="MsoNormal"><span> </span></p>
<p class="MsoNormal"><span>Danial Faghihi, University at Buffalo</span></p>
<p class="MsoNormal"><span>Alireza Tabarraei, University of North Carolina at Charlotte </span></p>
<p class="MsoNormal"><span>Kathryn Maupin, Sandia National Laboratories</span></p>
</div></div></div>Thu, 19 Mar 2020 03:37:00 +0000danialfaghihi24063 at https://imechanica.orghttps://imechanica.org/node/24063#commentshttps://imechanica.org/crss/node/24063PhD Positions in Computational Engineering at the University at Buffalo for Fall 2020
https://imechanica.org/node/23782
<div class="field field-name-taxonomy-vocabulary-6 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/73">job</a></div></div></div><div class="field field-name-taxonomy-vocabulary-8 field-type-taxonomy-term-reference field-label-hidden"><div class="field-items"><div class="field-item even"><a href="/taxonomy/term/2443">Computational Engineering</a></div><div class="field-item odd"><a href="/taxonomy/term/1788">applied mechanics</a></div><div class="field-item even"><a href="/taxonomy/term/3568">additive manufacturing</a></div><div class="field-item odd"><a href="/taxonomy/term/9229">biomechancis</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><p><span data-preserver-spaces="true">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.</span></p>
<p><span data-preserver-spaces="true">The projects aim at developing new theoretical and computational methods for predictive modeling of complex materials systems. Of particular interests are predicting the responses of metal additive manufactured components as well as biomaterials, using a combination of enhanced continuum theories, finite element solutions, and Bayesian inference. Candidates should possess a master's degree in mechanical, civil, or other related engineering fields at the time of enrollment at UB. A strong background in computational and applied mechanics is desired. If interested, contact Dr. Faghihi directly at </span><a class="_e75a791d-denali-editor-page-rtfLink" target="_blank">danialfa@buffalo.edu</a><span data-preserver-spaces="true">. Please include a CV along with a brief description of prior research experiences.</span></p>
</div></div></div>Thu, 28 Nov 2019 03:12:23 +0000danialfaghihi23782 at https://imechanica.orghttps://imechanica.org/node/23782#commentshttps://imechanica.org/crss/node/23782Error | iMechanica