Big Data

https://doi.org/10.1039/D0TB00896F It is my privilege and honor to be highlighted as the Journal of Material Chemistry B's Emerging Investigators for 2020. Together with our group's young budding scientists, Chenxi Zhai, Tianjiao Li, and Haoyuan Shi, we review the discovery and design of next-generation bio-inspired materials by harnessing the virtual space in materials design: materials omics (materiomics), materials informatics, computational modelling and simulations, artificial intelligence (AI), and big data.

Dear colleagues, 

we would like to make you aware of the topical session 

"Data driven materials science"

which is part of the MM program during the DPG Spring Meeting 2020. The latter takes place March 15-20, 2020, in Dresden.  

If you are performing experiments or simulations in this emerging field, you are most welcome to contribute your abstract. You can find the session at the bottom of the list "Themenbereiche" on the abstract submission webpage 

<p><a href="http://hdl.handle.net/10993/38545">http://hdl.handle.net/10993/38545</a…;
<p>Training at the European Commission&nbsp;</p>
<p>Upskilling in Data Science</p>
<p>Enjoy!</p>
<p>Stéphane</p>

The candidate will work in a project related with smart manufacturing of composite materials by injection/infusion techniques. The project will consist in a joint collaboration between Data Mining Research Group from Universidad Politécnica de Madrid (UPM) and IMDEA Materials Institute. The research activities will be focussed on the application of simulation techniques (computational fluid mechanics) to the optimization of manufacturing process of composite materials by liquid moulding, including the creation of a physical demonstrator.

The candidate will work in a project related with smart manufacturing of composite materials by injection/infusion techniques. The project will consist in a joint collaboration between Data Mining Research Group from Universidad Politécnica de Madrid (UPM) and IMDEA Materials Institute.The research activities will be focussed on the application of simulation techniques (computational fluid mechanics) to the optimization of manufacturing process of composite materials by liquid moulding, including the creation of a physical demonstrator.

Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today’s electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials.