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Fully funded PhD Positions within HetSys Centre for Doctoral Training at Warwick for October 2022 start

Submitted by Lukasz_Figiel on

HetSys is a recently established EPSRC-supported Centre for Doctoral Training (CDT) which trains people to challenge current state-of-the-art in computational modelling of heterogeneous, real-world systems across a range of research themes which this year include nanoscale devices, superalloys, laser-plasma interactions etc and future medicines.

 

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

Submitted by Boyang Chen on

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

Specimen Alignment in Material Testing

Submitted by Deniz Yalcin on

Specimen alignment plays a critical role in material testing. If a specimen is not properly aligned prior to or during testing, the accuracy and the reliability of test results will be directly affected, and, depending on the method followed, results may not be acceptable. This post covers the effect of specimen misalignment on the test data, main reasons behind axial misalignment, and ways to improve alignment precision in mechanical test setups.

Molecular dynamics study on the shock induced spallation of polyethylene

Submitted by Nuwan Dewapriya on

Our latest article in the Journal of Applied Physics is freely available for 14 days: https://aip.scitation.org/doi/10.1063/5.0072249

 We conducted molecular dynamics simulations of plate impact tests of polyethylene to obtain molecular-level insights on two common approximations associated with the interpretation of shock pressure and spall strength. Our results revealed

(1) The free surface approximation can slightly underpredict the shock pressure in the polymer.

Professor N. Sukumar: Meshfree analysis on complex geometries using physics-informed deep neural networks

Submitted by PedroAreias on

Meshfree analysis on complex geometries using physics-informed deep neural networks

Professor N. Sukumar 
University of California at Davis