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Open positions within the ERC project SLIDE: Adv ... (No replies)

mcrighi
5 years ago
mcrighi 5 years ago

 

Open positions within the ERC project “Advancing Solid Interfaces and Lubricants by First Principles Material Design” SLIDE are available at the Physics department of Bologna University, Italy.

Applications including CV and the addresses of at least two references should be sent to prof. M. Clelia Righi [email protected]

Two fully funded PhD positions are available starting from November 1st, 2020. Applicants with a Master's degree in Physics, Chemistry or Material Science should apply before May 18th, 2020.

Two Post Doc positions renewable up to four years are also available. Applicants should have expertise in some of the following:

  • Density functional theory modeling of materials, especially surfaces/interfaces and molecule-surface interactions. 
  • Molecular dynamics
  • High-performance computing.

Close collaboration with industry is envisaged for both types of positions.

Few words on the SLIDE project:

Friction and wear result in massive economic and environmental costs. By advancing tribological materials impressive energy savings, and consequent reduction of CO2 emissions can be obtained. However, optimizing lubricant materials is challenging because their performances are ruled by molecular-level processes that occur at the buried interface, which are extremely difficult to monitor by experiments. Simulations can play a decisive role here, in particular those based on quantum mechanics, which is essential to accurately describe reactions in conditions of enhanced reactivity as those imposed by the mechanical stresses applied.

The goals of SLIDE are: i) Optimize and extend the hybrid computer programs developed by our group to perform multiscale simulations of sliding interfaces and apply them for harnessing tribochemical reactions and design environmental-friendly lubricants. ii) Develop and apply a workflow for high throughput screening of material interfaces. A database will be created and used to train machine-learning algorithms. Chemical surface modifications will be identified to provide selected values of adhesion and shear strength.




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Ab initio (from electronic structure) calculation of complex processes in materials