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Four (4) PhD positions in AI @ TU Delft

Hiring four (4) new PhD students to join us at MACHINA: the new TUDelft AI lab that I am leading.


Each position is unique and has different co-advisors. Pick wisely ;)


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Journal Club for February 2020: Machine Learning in Mechanics: simple resources, examples & opportunities

Machine learning (ML) in Mechanics is a fascinating and timely topic. This article follows from a kind invitation to provide some thoughts about the use of ML algorithms to solve mechanics problems by overviewing my past and current research efforts along with students and collaborators in this field. A brief introduction on ML is initially provided for the colleagues not familiar with the topic, followed by a section about the usefulness of ML in Mechanics, and finally I will reflect on the challenges and opportunities in this field.

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[Deadlines updated] ICTAM2020 & WCCM2020

Dear colleagues,

Deadline to submit your abstract to ICTAM2020 and WCCM2020 is fast approacing (January 20 & 15, respectively). If you are working with machine learning, uncertainty quantification, optimization or a related topic, consider the following symposia:

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PhD position @ TU Delft: Data-driven design of materials and structures

Advertising the first fully funded PhD position in my group: this position is for the more computationally/mathematically inclined. Goal: method development.

A "general audience" summary of a recent application of our work:

Details about the position:

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Postdoctoral position @ TU Delft

Dear colleagues,

Richard Norte and I are looking for a postdoctoral scholar with interest in machine learning and good knowledge on finite element analyses.

This project is focused on computational mechanics in collaboration with a strong group on opto-mechanical devices.

For more information please check the following link:

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[Deadline: September 30, 2018] Tenure-track Assistant Professor Position @ TU Delft - Mechanical, Maritime and Materials Engineering

Dear colleagues,

New Assistant Professor position with experimental focus on polymers or polymer matrix composites at TU Delft.

Examples of areas of interest (not in any particular order):

- polymers or polymer composites under extreme events (high strain-rates, extreme temperatures, fire, radiation, etc.)

- recycling and contributions to circular economy

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[June 5 | USNC/TAM2018] Short-course on data-driven modeling in mechanics and materials science

Dear colleagues in academia and industry,

We are organizing the first short course on data-driven computational mechanics and materials science at the 18th US National Congress on Theoretical and Applied Mechanics (USNC/TAM) in Chicago on June 5th, 2018. See description of short-course SC007 in the following website:

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[Abstract submission deadline: January 15] MS1801 @ WCCM2018 -> Data-driven Methods and Applications: from Physics-informed Learning Machines to Optimization Under Uncertainty

Dear colleagues,

The deadline to submit your abstracts to WCCM 2018 is January 15, 2018. We would like to once again encourage you to submit your abstracts to minisymposium 1801. For more information please go to:

Best regards,

Miguel Bessa, Delft University of Technology

George Karniadakis, Brown University

Julien Yvonnet, Université Paris-Est

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LEaDing Fellows postdoctoral opportunity [deadline January 10, 2018]

I would like to raise your attention to the following prestigious European fellowship opportunity:

-> 90 postdoctoral positions available for applicants from all over the world to work for two years in the best research universities in the Netherlands. Applicants choose the research topic and advisor to work on the application.

Additional information on this call for proposals:

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MS1801 @ WCCM2018 -> Data-driven Methods and Applications: from Physics-informed Learning Machines to Optimization Under Uncertainty

Dear colleagues,

We encourage you to submit your abstracts to minisymposium 1801 of the 13th World Congress on Computational Mechanics (New York City, from July 22 to 27 of 2018). This minisymposium focuses on:

1. recently developed methods for data-driven approaches;

2. data-driven applications to fluids, structures and materials involving (but not limited to) machine learning, uncertainty quantification and/or optimization.

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