-
: Education: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science. Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods. Proven experience in
-
Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
Personal Competences: Demonstrated competitive ability in using DFT simulations, and machine learning techniques and DFT. Demonstrated strong coding skills and a passion for UX/UI design. Summary
-
. Knowledge and Professional Experience: Interest of learning (S)TEM, EM related spectroscopies and in-situ techniques (use of gas and/or liquid, bias and heating STEM sample holders). Previous experience
-
to provide internal operational guidance and advanced tools that accelerate the adoption and development of machine learning (ML) methods across the centre. Its mission is supported by a set of strategic lines
-
parameters affect material properties and functional performance, and interacting with machine-learning and modelling teams to translate experimental results into predictive datasets. Preparing reproducible
-
integrating machine learning (ML) and molecular dynamics (MD) tools with experimental feedback, the project strives to accelerate the design of efficient and sustainable nanocatalysts, contributing
-
or similar. Knowledge and Professional Experience: Programming Languages: Solid knowledge in at least one of the following environments (and willingness to learn others): Python (Django), PHP (Laravel