Quercus Hernández, best thesis award from SEMNI

Dear colleagues,

last Friday, March 1st, the SEMNI Executive Committee held an extraordinary meeting to designate the thesis that will receive the SEMNI award in its 2023 edition and that, therefore, will compete as our representative for the ECCOMAS award.

As you know, SEMNI appoints a committee chaired by a member of the Executive Committee, a task that fell to Prof. Francisco Montans on this occasion, and formed by former recipients of the Juan Carlos Simó award for young researchers that SEMNI awards annually. This year the committee was made up of
– Emilio Martínez Pañeda, Oxford U.
– Marcos Latorre, U. P. Valencia
– Esther Reina, U. Sevilla
– María José Gómez, U. Zaragoza
– Xesús Nogueira, University of Coruña

The committee proposed to the executive committee that the prize should go to the thesis “Structure-preserving Deep Learning”, by Quercus Hernández. The thesis can be consulted from the following link, 

We would like to congratulate Quercus and wish him the best of luck in his candidacy for the ECCOMAS prize.

The prize, consisting of a diploma and a cheque for one thousand euros, will be awarded during the gala dinner of the Congress on Numerical Methods to be held in Aveiro, Portugal, next September.

Post-doctoral opening


“Physics-informed Artificial Intelligence for Cognitive Twins of Complex Systems”

The Applied Mechanics and Bioengineering (AMB) group of the University of Zaragoza (see http://amb.unizar.es) is one of the leading groups within the computational mechanics community at a national and European levels, comprised by 20 senior faculty members and a variable number of Ph.D. students and post-doctoral associates.

The group has a broad experience in the development of advanced simulation techniques such as meshless methods, model order reduction and, more recently, scientific machine learning for the simulation of complex, multi physics and multiscale phenomena.

With the recently awarded project Physics-informed Artificial Intelligence for cognitive twins of complex systems we aim at developing novel tools for the machine learning of complex physical phenomena with guaranteed physical meaning.

In the frame of this emergent project in the group, we seek a highly motivated postdoctoral candidate to develop and implement artificial intelligence tools able to achieve physical perception and reasoning about complex physical phenomena taking place in the neighborhood of this AI.


The candidate, in close collaboration with profs. E. Cueto, D. González, I. Alfaro, and the rest of the team, will develop advanced AI techniques based on physics-informed neural networks towards the creation of cognitive digital twins, able to understand the surrounding environment and adapt to changing scenarios. 


Applications/interviews: Please send an email expressing your interest, together with a CV to Prof. Elías Cueto (ecueto@unizar.es). Reviews of applications will continue until the position is filled.

Initial date: As son as possible. 

Duration: 2 years. 

Dedication: Full-time, 40 h/week. 

Gross annual salary: 36 149 € (including Spanish public health and social security benefits). 


Hard / Essential: 

• PhD in Computational Continuum Mechanics (or similar), with a strong numerical focus on mathematical modeling. 
• In-depth, hands-on knowledge of modeling software and computer programming (MATLAB, Python, C/C++, Fortran) as well as of the Finite Element Method and its procedures. 

Hard / Desirable: 

• Knowledge about neural network programming (preferably, PyTorch).
• Knowledge on Computer Vision.
• Computational Solid Mechanics. 
• Image Processing and Data Analysis. 

Soft / Essential: 

• Keen interest in learning novel computational methods. 
• Self-directed with the ability to work independently. 
• At the same time, ability to work in group and (co-)advise doctoral students.
• Excellent communication and writing skills in English. 


If interested, please send your CV, a cover letter, and a reference letter to Elías Cueto (ecueto@unizar.es).

Digital twins for fluid sloshing problems

Teaching robots to manipulate liquids (to help elderly people, for instance) is a challenge. We are used to recognize different fluids at first sight, but this educated intuition is difficult to transmit to robots.

Our last job has been just published in the Public Library of Science (PLOS)ONE journal (Open Access): https://lnkd.in/g8VY9ns

We present a #digitaltwin for fluid sloshing that, through computer vision and making an extensive use of simulation in the background, is able to learn the particular fluid and its behavior—even if the robot has not been exposed to it before—and predict its dynamics in the near future.

A great job by BEATRIZ MOYA, David González, Iciar Alfaro, and Francisco Chinesta, under the financial support from ESI Group, which is gratefully acknowledged.