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For its second edition, the DRIVEN workshop will take place in Luxembourg the 11th and 12th of September 2019.

The theme of the workshop will mostly talk about how to use digital twins from the medical field to dynamic simulations.

ERCIM offers fellowships for PhD holders from all over the world. The next round is open!

Deadline for applications: 30 September 2019.

Topics cover most disciplines in Computer Science, Information Technology, and Applied Mathematics.
Fellowships are of 12-month duration spent in one ERCIM member institute.

Detailed description of the programme, application guide and application form : http://fellowship.ercim.eu/

Conditions
Applicants must:

• have obtained a PhD degree during the last 8 years (prior to the application year deadline) or be in the last year of the thesis work with an outstanding academic record. Nevertheless, before starting the grant, a proof of the PhD degree will be requested.
• complete, submit the application form and send via the online system:
  • a detailed curriculum vitae
  • a list of publications
  • two scientific papers in English
  • contact details of two referees
• start the fellowship no later than 1 May 2020.

We would like to bring to your attention our upcoming IWR School which will focus on machine
learning with applications from Natural Sciences and Life Sciences. The school will take place
from September 23 to 27, 2019, in Heidelberg, and addresses young researchers (PhD,
PostDoc, Master) from Natural and Life Sciences who want to learn more about machine
learning.
A background in Machine Learning is not required. Besides introducing the basic concepts we
teach selected topics in more depth, such as deep learning, metric learning, transfer learning,
Bayesian inverse problems, and causality. Experts from machine learning, Natural Science and
Life Science explain how these machine learning approaches are utilized to solve problems in
their respective fields of research.

Further information on the IWR School 2019 are available at:
www.iwr.uni-heidelberg.de/events/iwr-school-2019

It was a pleasure to welcome Michael Ortiz from the California Institute of Technology to give a seminar on the topic of “Model-Free Data-Driven Computing” at the University of Luxembourg. You can watch the entire seminar below.

Abstract:

We develop a new computing paradigm, which we refer to as Data-Driven Computing, according to which calculations are carried out directly from experimental material data and pertinent kinematic constraints and conservation laws, such as compatibility and equilibrium, thus bypassing the empirical material modeling step of conventional computing altogether. Data-driven solvers seek to assign to each material point the state from a prespecified data set that is closest to satisfying the conservation laws. Equivalently, data-driven solvers aim to find the state satisfying the conservation laws that is closest to the data set. The resulting data-driven problem thus consists of the minimization of a distance function to the data set in phase space subject to constraints introduced by the conservation laws. We demonstrate the data-driven paradigm and investigate the performance of data-driven solvers by means of several examples of application, including statics and dynamics of nonlinear three-dimensional trusses, and linear and nonlinear elasticity. In these tests, the data-driven solvers exhibit good convergence properties both with respect to the number of data points and with regard to local data assignment, including noisy material data sets containing outliers. The variational structure of the data-driven problem also renders it amenable to analysis. We find that the classical solutions are recovered in the case of linear elasticity. We identify conditions for convergence of Data-Driven solutions corresponding to sequences of approximating material data sets. Specialization to constant material data set sequences in turn establishes an appropriate notion of relaxation. We find that relaxation within the Data-Driven framework is fundamentally different from the classical relaxation of energy functions. For instance, we show that in the Data-Driven framework the relaxation of a bistable material leads to effective material data sets that are not graphs. I will finish my presentation with highlights on work in progress, including closed-loop Data-Driven analysis and experiments, Data-Driven molecular dynamics, Data-Driven inelasticity and publicly-editable material data repositories and data management from a Data-Driven perspective.

Thank you very much to our guest panelists and to the ERC candidates.

Computational and Data Science Seminar. Igor Peterlik from Inria discusses data assimilation for surgical simulation.

https://wwwde.uni.lu/fstc/actualites/modeliser_le_cerveau_avec_des_briques_de_lego

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