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PhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable Fibrous Composites
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PhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable Fibrous Composites
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PhDFinder PhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable Fibrous Composites
1 day ago Be among the first 25 applicants Join to apply for the
PhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable Fibrous Composites
role at
PhDFinder Get AI-powered advice on this job and more exclusive features. University:
Eindhoven University of Technology
Country:
Netherlands
Deadline:
2025-08-24
Fields:
Mechanical Engineering, Civil Engineering, Computational Mechanics, Mathematical Engineering, Materials Science
Are you passionate about harnessing advanced computational methods and machine learning to solve real-world engineering challenges? Do you aspire to contribute to the development of sustainable materials that shape the future of construction, automotive, and consumer industries? If your academic ambitions include working at the intersection of mechanics, data science, and sustainability, this opportunity at Eindhoven University of Technology may be your ideal next step.
About The University Or Research Institute
Eindhoven University of Technology (TU/e) is internationally recognized as a top-ranking institution in the Netherlands, renowned for its scientific curiosity and practical, hands-on approach. Located in the vibrant city of Eindhoven, TU/e is celebrated for its collaborative spirit, open culture, and strong partnerships with advanced industries. The university holds a top-five position in industry collaboration and is committed to addressing highly complex problems with innovative solutions. The Department of Built Environment at TU/e is a leader in sustainability-focused research and education, integrating technology, engineering, design, and human behavior to create future-proof, inclusive, and environmentally respectful built environments. The department boasts world-class experimental facilities and fosters interdisciplinary research, providing an exceptional academic environment for aspiring researchers.
Research Topic and Significance
The focus of this PhD project is on the multiscale and multiphysics modeling of sustainable fibrous composites, emphasizing uncertainty quantification and the acceleration of simulations using machine learning. Natural fiber-reinforced composites, such as those made from hemp, flax, or bamboo, are gaining prominence as environmentally friendly alternatives to traditional composite materials. These materials are pivotal in construction (for insulation and load-bearing components), automotive (for interior parts), and consumer products (like sports equipment), offering sustainability and circularity benefits. However, their performance is influenced by moisture, chemical degradation, and significant variability in their microstructure and properties, making predictive modeling a complex, multidisciplinary task. This research addresses these challenges by developing advanced computational models and integrating machine learning to enable faster, more accurate predictions, ultimately supporting the adoption of sustainable materials in society.
Project Details
The successful candidate will join the chair of Applied Mechanics within the Department of Built Environment at TU/e, working under the supervision of Dr. Emanuela Bosco and Dr. Payam Poorsolhjouy, with additional guidance from Dr. Lars Beex (University of Luxembourg). The Applied Mechanics group specializes in multiscale, multiphysics, and optimization problems related to the built environment and is a member of the Graduate School of Engineering Mechanics, Netherlands. This graduate school provides PhD students with advanced training in engineering mechanics through a joint series of courses aligned with cutting-edge research themes.
The Project Offers Flexibility To Focus On One Or More Of The Following Areas, Depending On The Candidate’s Expertise And Interests
– Developing numerical models for the coupled mechanical-diffusive(-thermal-chemical) behavior of fiber-reinforced composites at the micro/meso-scale, incorporating uncertainty via stochastic inputs (e.g., random field representations).
– Analyzing model responses using techniques such as Monte Carlo simulations.
– Identifying variability in model parameters through Bayesian inference.
– Quantifying the impact of microscale uncertainties on macroscale material performance using stochastic homogenization and uncertainty propagation methods, including Monte Carlo and Gaussian Process-based approaches.
– Integrating machine learning techniques to replace computationally expensive simulations and enable faster predictions while preserving uncertainty information.
The candidate will collaborate closely with other PhD students working on related aspects of heterogeneous material behavior, benefiting from a vibrant, interdisciplinary research community.
Candidate Profile
Applicants should be talented, motivated, and enthusiastic researchers with the following qualifications:
– MSc degree in Mechanical Engineering, Civil Engineering, Computational Mechanics, Mathematical Engineering, or an equivalent field.
– Strong background in mechanics of materials, as well as multi-scale and multi-physics methods.
– Analytical skills, initiative, and creativity are highly valued.
– Additional expertise in uncertainty quantification and/or machine learning techniques is advantageous.
– Interest in interdisciplinary projects that integrate analytical/computational frameworks, uncertainty quantification, and machine learning to address challenges with significant social and industrial impact.
– Excellent oral and written communication skills in English (C1 level).
Application Process
To apply, please submit a complete application through the official TU/e application portal by 24-08-2025. Your application should include:
– Cover letter describing your motivation and qualifications for the position.
– Curriculum vitae, including a list of publications and contact information of three references.
– Brief description of your MSc thesis.
– Transcripts of BSc and MSc programs, including courses taken and grades achieved.
For more information and to apply, please visit: https://www.tue.nl/en/working-at-tue/vacancy-overview/phd-on-ml-accelerated-simulations-and-uncertainty-quantification-of-composites
Conclusion
This PhD position offers a unique opportunity to contribute to the advancement of sustainable materials through cutting-edge computational and machine learning approaches. If you are eager to make a meaningful impact in engineering and sustainability research, we encourage you to apply and become part of TU/e’s dynamic academic community. For more opportunities like this, explore the latest openings in your field.
Get the latest openings in your field and preferred country—straight to your email inbox. Sign up now for 14 days free: https://phdfinder.com/position-alert-service/
We’re an independent team helping students find opportunities.
Found this opportunity helpful? Support us with a coffee!
Also See
Postdoctoral Opportunity in In-Silico Clinical Trials for Cardiovascular Engineering at RWTH… PhD Research Fellow in Energy Technology Management at University of South-Eastern Norway Multiple PhD Positions in Advanced Infrastructure Materials at the University of Miami PhD and Postdoctoral Opportunities in Experimental & Computational Mechanics of… PhD Position in Distributed Continual Machine Learning at Uppsala University
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Manchester, TN $83,250.00-$126,600.00 2 months ago Tullahoma, TN $135,000.00-$180,000.00 6 days ago Tullahoma, TN $140,000.00-$190,000.00 1 day ago Tullahoma, TN $140,000.00-$190,000.00 1 day ago Tullahoma, TN $135,000.00-$180,000.00 6 days ago Tullahoma, TN $140,000.00-$190,000.00 1 day ago Manchester, TN $102,000.00-$168,000.00 1 month ago PhD Position in Hierarchical Diagnostics of Complex Dynamical Systems at Eindhoven University of Technology
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
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Join to apply for the
PhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable Fibrous Composites
role at
PhDFinder PhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable Fibrous Composites
1 day ago Be among the first 25 applicants Join to apply for the
PhD Position in ML-Accelerated Simulations and Uncertainty Quantification of Sustainable Fibrous Composites
role at
PhDFinder Get AI-powered advice on this job and more exclusive features. University:
Eindhoven University of Technology
Country:
Netherlands
Deadline:
2025-08-24
Fields:
Mechanical Engineering, Civil Engineering, Computational Mechanics, Mathematical Engineering, Materials Science
Are you passionate about harnessing advanced computational methods and machine learning to solve real-world engineering challenges? Do you aspire to contribute to the development of sustainable materials that shape the future of construction, automotive, and consumer industries? If your academic ambitions include working at the intersection of mechanics, data science, and sustainability, this opportunity at Eindhoven University of Technology may be your ideal next step.
About The University Or Research Institute
Eindhoven University of Technology (TU/e) is internationally recognized as a top-ranking institution in the Netherlands, renowned for its scientific curiosity and practical, hands-on approach. Located in the vibrant city of Eindhoven, TU/e is celebrated for its collaborative spirit, open culture, and strong partnerships with advanced industries. The university holds a top-five position in industry collaboration and is committed to addressing highly complex problems with innovative solutions. The Department of Built Environment at TU/e is a leader in sustainability-focused research and education, integrating technology, engineering, design, and human behavior to create future-proof, inclusive, and environmentally respectful built environments. The department boasts world-class experimental facilities and fosters interdisciplinary research, providing an exceptional academic environment for aspiring researchers.
Research Topic and Significance
The focus of this PhD project is on the multiscale and multiphysics modeling of sustainable fibrous composites, emphasizing uncertainty quantification and the acceleration of simulations using machine learning. Natural fiber-reinforced composites, such as those made from hemp, flax, or bamboo, are gaining prominence as environmentally friendly alternatives to traditional composite materials. These materials are pivotal in construction (for insulation and load-bearing components), automotive (for interior parts), and consumer products (like sports equipment), offering sustainability and circularity benefits. However, their performance is influenced by moisture, chemical degradation, and significant variability in their microstructure and properties, making predictive modeling a complex, multidisciplinary task. This research addresses these challenges by developing advanced computational models and integrating machine learning to enable faster, more accurate predictions, ultimately supporting the adoption of sustainable materials in society.
Project Details
The successful candidate will join the chair of Applied Mechanics within the Department of Built Environment at TU/e, working under the supervision of Dr. Emanuela Bosco and Dr. Payam Poorsolhjouy, with additional guidance from Dr. Lars Beex (University of Luxembourg). The Applied Mechanics group specializes in multiscale, multiphysics, and optimization problems related to the built environment and is a member of the Graduate School of Engineering Mechanics, Netherlands. This graduate school provides PhD students with advanced training in engineering mechanics through a joint series of courses aligned with cutting-edge research themes.
The Project Offers Flexibility To Focus On One Or More Of The Following Areas, Depending On The Candidate’s Expertise And Interests
– Developing numerical models for the coupled mechanical-diffusive(-thermal-chemical) behavior of fiber-reinforced composites at the micro/meso-scale, incorporating uncertainty via stochastic inputs (e.g., random field representations).
– Analyzing model responses using techniques such as Monte Carlo simulations.
– Identifying variability in model parameters through Bayesian inference.
– Quantifying the impact of microscale uncertainties on macroscale material performance using stochastic homogenization and uncertainty propagation methods, including Monte Carlo and Gaussian Process-based approaches.
– Integrating machine learning techniques to replace computationally expensive simulations and enable faster predictions while preserving uncertainty information.
The candidate will collaborate closely with other PhD students working on related aspects of heterogeneous material behavior, benefiting from a vibrant, interdisciplinary research community.
Candidate Profile
Applicants should be talented, motivated, and enthusiastic researchers with the following qualifications:
– MSc degree in Mechanical Engineering, Civil Engineering, Computational Mechanics, Mathematical Engineering, or an equivalent field.
– Strong background in mechanics of materials, as well as multi-scale and multi-physics methods.
– Analytical skills, initiative, and creativity are highly valued.
– Additional expertise in uncertainty quantification and/or machine learning techniques is advantageous.
– Interest in interdisciplinary projects that integrate analytical/computational frameworks, uncertainty quantification, and machine learning to address challenges with significant social and industrial impact.
– Excellent oral and written communication skills in English (C1 level).
Application Process
To apply, please submit a complete application through the official TU/e application portal by 24-08-2025. Your application should include:
– Cover letter describing your motivation and qualifications for the position.
– Curriculum vitae, including a list of publications and contact information of three references.
– Brief description of your MSc thesis.
– Transcripts of BSc and MSc programs, including courses taken and grades achieved.
For more information and to apply, please visit: https://www.tue.nl/en/working-at-tue/vacancy-overview/phd-on-ml-accelerated-simulations-and-uncertainty-quantification-of-composites
Conclusion
This PhD position offers a unique opportunity to contribute to the advancement of sustainable materials through cutting-edge computational and machine learning approaches. If you are eager to make a meaningful impact in engineering and sustainability research, we encourage you to apply and become part of TU/e’s dynamic academic community. For more opportunities like this, explore the latest openings in your field.
Get the latest openings in your field and preferred country—straight to your email inbox. Sign up now for 14 days free: https://phdfinder.com/position-alert-service/
We’re an independent team helping students find opportunities.
Found this opportunity helpful? Support us with a coffee!
Also See
Postdoctoral Opportunity in In-Silico Clinical Trials for Cardiovascular Engineering at RWTH… PhD Research Fellow in Energy Technology Management at University of South-Eastern Norway Multiple PhD Positions in Advanced Infrastructure Materials at the University of Miami PhD and Postdoctoral Opportunities in Experimental & Computational Mechanics of… PhD Position in Distributed Continual Machine Learning at Uppsala University
Seniority level
Seniority level Internship Employment type
Employment type Full-time Job function
Job function Research, Analyst, and Information Technology Industries Education Referrals increase your chances of interviewing at PhDFinder by 2x Sign in to set job alerts for “PHD” roles.
Lecturer, Humanities and Social Sciences
PhD Position in Climate-Resilient Stormwater Management
PhD Position in Time-Constrained Microservice Execution
Postdoctoral Opportunity in Robotics and Artificial Intelligence at Luleå University of Technology
Fully Funded PhD Scholarship in Quantum Physics – Polarons at Swinburne University of Technology
PhD Positions in Metasurfaces for Biosensing at the Department of Applied Physics
Postdoctoral Position in Wearable Optical Sensing Technology
Fully Funded Master’s and PhD Positions in Quantum Machine Learning and AI under Uncertainty
Postdoctoral Scholarship in Waste Science & Technology: Electrochemical Degradation of Persistent Organic Pollutants Using Upcycled Titanium
PhD Position in Terahertz Imaging Transmitter Based on Photonic Integrated Circuits
Manchester, TN $83,250.00-$126,600.00 2 months ago Tullahoma, TN $135,000.00-$180,000.00 6 days ago Tullahoma, TN $140,000.00-$190,000.00 1 day ago Tullahoma, TN $140,000.00-$190,000.00 1 day ago Tullahoma, TN $135,000.00-$180,000.00 6 days ago Tullahoma, TN $140,000.00-$190,000.00 1 day ago Manchester, TN $102,000.00-$168,000.00 1 month ago PhD Position in Hierarchical Diagnostics of Complex Dynamical Systems at Eindhoven University of Technology
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
#J-18808-Ljbffr