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Zilin Yan's picture

PhD and Postdoc Positions at Harbin Institute of Technology, Shenzhen

The Mechanics of Functional Ceramics group ( at HITsz, led by Dr. Zilin Yan, Associate Professor, is seeking highly motivated postdoctoral researchers and Ph.D. students for three directions.


Postdoc position in Machine Learning for Advanced Physical Simulation at Aarhus University, Denmark

I have an opening in my group for a 2-year postdoc position offering applicants an exciting opportunity to join my ERC-funded project “ALPS - AI-based Learning for Physical Simulation”.

Job description

You will be contributing to developing novel algorithms at the intersection of computational physics and machine learning for the automatic identification of symbolic models of physical systems starting from experimental data.

matthew.grasinger's picture

Summer research opportunities in machine learning and computational mechanics at AFRL

The DoD HPC Modernization Program has high-performance computing internship opportunities at the Air Force Research Laboratory. These internships give undergraduate and graduate students the opportunity to perform scientific, computational research alongside AFRL researchers in support of the US Air Force’s mission.

Hanxun Jin's picture

Journal Club for January 2024: Machine Learning in Experimental Solid Mechanics: Recent Advances, Challenges, and Opportunities

Hanxun Jin (a,b), Horacio D. Espinosa (b)
a Division of Engineering and Applied Science, California Institute of Technology
b Department of Mechanical Engineering, Northwestern University

In recent years, Machine Learning (ML) has become increasingly prominent in Solid Mechanics. Its diverse applications include extracting unknown material parameters, developing surrogate models for constitutive modeling, advancing multiscale modeling, and designing architected materials. In this Journal Club, we will focus our discussion on the recent advances and challenges of ML when experimental data is involved. With broad community interest, as reflected by the increasing number of publications in this field, we have recently published a review article in Applied Mechanics Reviews titled “Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review”. Moreover, a recent insightful paper from Prof. Sam Daly’s group also discussed some perspectives in this field. In this Journal Club, we would like to introduce and share insights into this exciting field.

Call for abstract submission to WCCM/PANACM 2024: MS1815 Machine learning algorithms for accelerating material characterization, discovery, design, and manufacturing processes

Dear Colleagues, 

We cordially invite you to submit an abstract to our Mini-Symposium: MS1815, "Machine learning algorithms for accelerating material characterization, discovery, design, and manufacturing processes," for the 16th World Congress on Computational Mechanics (WCCM-PANACM 2024) held on July 21-26, 2024, in Vancouver, BC, Canada. 

The deadline for submission of Abstracts is January 15, 2024. 

miquel.aguirre's picture

Postdoc position at CIMNE Barcelona in data-driven modelling for endovascular thrombectomy

We are looking for a postdoctoral researcher to work on the project MECA-ICTUS, a 3-year project funded under the Generación de Conocimiento 2022 call of Agencia Estatal de Investigación. In MECA-ICTUS we will pursue the development of computational mechanics and machine learning tools for predicting the success of endovascular thrombectomy, an urgent intervention for the removal of thrombi in Acute Ischemic Stroke Patients.

Postdoc/PhD positions on granular materials and computational mechanics, Tsinghua University

Multiple postdoc and one PhD positions are open at Tsinghua University. The research will take place at Tsinghua University’s Shenzhen International Graduate School (SIGS), located in Shenzhen, China, and is partly sponsored by NSFC and Tsinghua SIGS’s scientific research startup funds.

Postdoc (2 years) on Machine learning techniques for interpretation of vibrational data

Additive manufacturing of metal alloys yields great potential for the aerospace industry (and others) as it allows the generation of geometrically complex structures with high specific strength, low density and high corrosion resistance. For example, General Electric has demonstrated the possibility of printing titanium fuel injectors for their LEAP engine, Boeing incorporated more than 300 printed parts in their 777X airplane … For such critical applications, the structural quality of printed parts is of utmost importance. Small deviations in print conditions, e.g.

postdoctoral and Ph.D. positions in machine learning, multi-physics simulation and uncertainty quantification for additive manufacturing processes

We invite applications for post-doctoral and Ph.D. positions in machine learning, multi-physics simulation and uncertainty qualification for additive manufacturing processes. 

For additional information, please contact Prof. Xiaoping Qian ( To apply, email your application files (CV, 1-page summary of research accomplishments and research interests, and 3 references).

rudaz's picture

Postdoc and PhD positions in Uncertainty Quantification and Computational Mechanics - Houston, TX

The Uncertainty Quantification (UQ) Lab at the University of Houston (UH), led by Dr. Ruda Zhang, invites applications for:

  • two (2) PhD student positions in areas of data-driven engineering and uncertainty quantification, and
  • one (1) postdoctoral researcher in areas of surrogate modeling and data-driven dynamics.

For job details and updates, see lab webpage:

 PhD Students - 2023

A.Tabarraei's picture

Ph.D. Position at the Department of Mechanical Engineering at UNCC

Ph.D. positions are available at the Multiscale Material Modeling Lab at the University of North Carolina at Charlotte. The Ph.D. students will develop and use tools such as machine learning, finite elements, and molecular dynamics to study the mechanical properties of materials. Candidates with a strong background and interest in solid mechanics, programming, and computational solid mechanics are encouraged to submit their CV to In your CV please include the name and contact information of three references.  

PhD position in computational geomechanics and probabilistic methods at University of Cincinnati

Dr. Lei Wang’s research group at the University of Cincinnati (UC) is seeking two high-motivated and talented PhD students to be appointed as Graduate Assistants to conduct geotechnical engineering research in the Department of Civil and Architectural Engineering and Construction Management, beginning January 2023 or August 2023. 


enrico.salvati1's picture

Defect-based Physics-Informed Machine Learning Framework for Fatigue Prediction

I would like to draw your attention to our recently proposed predictive method based on a semi-empirical model (LEFM) and Neural Network, exploiting the Physics-informed Machine Learning concept. We show how the accuracy of state-of-the-art fatigue predictive models, based on defects present in materials, can be significantly boosted by accounting for additional morphological features via Physics-Informed Machine Learning.

Webinar: Automated Segmentation and Design Workflows for Patient-Specific Surgical Guides

Join the Simpleware Product Group at Synopsys and nTopology for a webinar this month on automated segmentation and design workflows for patient-specific surgical guides.

Learn more and register for this free event here:

Jaafar El-Awady's picture

Postdoc Position in machine learning informed materials design at Johns Hopkins University

I am seeking a motivated and qualified postdoctoral fellow with expertise in the field of machine learning (ML)-informed materials design. In particular, the postdoctoral fellow will work closely in collaboration with other experimentalists and modelers within the newly established AI for Materials Design (AIMD) facility at JHU for the design of new refractory multi-principal-element alloys (RMPEAs) by identifying their processing-structure-property-performance relationships for high-strain-rate and high-temperature-rate applications.

Webinar: AI Medical Image Segmentation with Simpleware Software: A Solutions Overview

Synopsys are running a webinar this month on using AI medical image segmentation in Simpleware software to scale-up patient-specific workflows, save time, and free up staff resources. Highly recommended if you haven't yet had a chance to review the tools in Simpleware software.

Register here:

elejeune's picture

​​Journal Club for May 2022: Machine Learning in Mechanics: curating datasets and defining challenge problems


Over the past several years, machine learning (ML) applied to problems in mechanics has massively grown in popularity. Here is a figure from a slide that I made in early 2020 referencing a few examples from the literature — already this slide feels out of date! All of these authors (and many many others) have published new papers on this topic. 

raynexzheng's picture

Ph.D/postdoc position in architected metamaterials at Berkeley

We have new postdoc/Ph.D opening immediately in the broad area of architected metamaterials. Candidates who have prior experience in the mechanics of architected materials, machine learning/optimizations and interests in applying various additive manufacturing techniques developed in our lab is especially encouraged to reach out to us (email:

Harley T. Johnson's picture

Postdoc positions in computational science at UIUC

Two postdoc positions in computational science are available at UIUC in the research group of Prof. Harley T. Johnson in the Department of Mechanical Science and Engineering and the Materials Research Lab.  

Boyang Chen's picture

4 fully-funded PhDs in TU Delft on Aerospace, Machine Learning and Quantum Computing

4 fully-funded PhDs are available in Delft University of Technology under the joint supervision of researchers from the Faculty of Aerospace Engineering and the Faculty of Electrical Engineering, Mathematics and Computer Science. We will explore the exciting interdisciplinary areas of Quantum Computing and M


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