Job announcements relevant to people interested in electronic structure calculations…
Due to the large number of posts recently, there is currently a delay of several weeks between posts being submitted and the corresponding emails being distributed to all users. Please bear with us while we try to improve this. In the meantime – and until this notice is removed – it would assist us considerably if you could submit only important and/or urgent posts and thus help to reduce the size of the mail queue. Under no circumstances should you resend posts multiple times when you find the emails are not distributed immediately.
In light of the Russian military offensive in Ukraine, we request that announcements relating to events, jobs and other activities associated with institutions supported by the Russian and Belarusian states are not posted to the Psi-k forum.
PhD position at CNR-PoliMi-ST (Italy) on "M ... (No replies)
Back to Job listings...
THESIS TITLE: “Design of Novel Multifunctional Materials: Ab-initio Simulations, High-throughput and Machine Learning Approaches for Bulk and Nanoscale Materials”
Within the Agreement between the Italian National Research Council (CNR), Confindustria and Universities, a PhD fellowship is sponsored within the XXXVII PhD cycle for the Academic Year 2021/2022. The work will be carried out jointly at Consiglio Nazionale delle Ricerche CNR-SPIN in Chieti (Dr. Silvia Picozzi), Politecnico di Milano (Prof. R. Bertacco) and STMicroelectronics in Agrate, Milano (Dr. Sandro Dalle Feste).
MOTIVATION
Materials design is currently undergoing a revolution, driven by the increasing importance of machine learning (ML) as a boost for materials discovery. While at present ML for quantum matter is rather unshaped, it is likely to dramatically develop in the next decade, with all materials’ classes (including functional compounds, such as ferroics) expected to benefit from artificial intelligence. Ferroic materials (magnets, ferroelectrics, etc) are particularly difficult to model on the basis of “intuition” or simple “rules of thumbs”, due to their inherent complexity. Furthermore, they often present cross-coupling phenomena (piezoelectricity, magnetoelectricity,..), which makes them useful for applications, but at the same time extremely difficult to predict and optimize without a complex theoretical machinery. The combination of ab-initio simulations based on density functional theory (DFT) and of ML represents therefore a powerful tool for future materials design.
RESEARCH ACTIVITY, OBJECTIVES AND TECHNIQUES
The theoretical activity will involve developing a code for ML oriented towards the detailed understanding of microscopic mechanisms underlying materials’ functionalities and towards design of functional compounds with optimized properties of technological relevance for microelectronics. The code, aimed at identifying via ML complex descriptors characterizing the property of interest, will be tested starting from different DFT databases present in the literature for functional materials, possibly incremented, for a better accuracy, via high-throughput DFT simulations performed by the candidate.
EDUCATIONAL OBJECTIVES
Development of interdisciplinary knowledge at the boundary between computational materials science, machine learning and applied physics.
REQUIRED EXPERTISE
ELIGIBILITY
Master degree in Physics (or related disciplines) officially obtained before April 30th, 2022, with threshold on minimum score. Certified knowledge of English. Info on Master score’s thresholds and accepted English certificates available from silvia.picozzi at spin.cnr.it (E-mail subject: CNR-PoliMi Joint PhD Fellowship).
EXPRESSIONS OF INTEREST
A Curriculum Vitae (including Master degree, related list of marks and English Certificate) should be sent to silvia.picozzi at spin.cnr.it (E-mail subject: CNR-PoliMi Joint PhD Fellowship).
STARTING DATE OF PHD PROGRAM: May 1st, 2022
Review of applications will start immediately.
INFO ON GROUP RESEARCH ACTIVITY:
https://sites.google.com/site/silviapicozzi/