-
, engineering or physics. Knowledge: Computational programming, machine learning, quantum transprot, device simulation. Professional Experience: use of device simulation codes applied to 2D materials. Personal
-
Physics, Materials Science or an equivalent field, with a background in condensed matter theory or computational nanoscience. Junior postdoctoral profile (R2). Knowledge: Spin-orbit torques and spintronics
-
: Education: PhD on Materials Science, Chemistry, Biotechnology, or related disciplines Knowledge: High level in English Professional Experience: Demonstrable previous experience in materials science
-
programme. Requirements: Education: PhD in chemistry, chemical engineering, materials science, biochemistry or equivalent. Knowledge: Extensive expert use of an array of physicochemical and materials
-
beyond conventional binary "bits" toward multi-level and analog memory systems that enable richer information encoding. A major thrust of the group is the development of in-memory and physical computing
-
· FEI F20 200 KeV STEM, EELS + EDX · Fully Automated FIB Helios 5UX · FEI SEM Quanta and SEM Magellan Requirements: Education: PhD in Physics, Materials Science, Nanoscience, Computer
-
students and other researchers on in-situ (S)TEM. Requirements: · Education: PhD in Chemistry, Physics or Material Science, or closely related fields, with a strong focus on nanomaterials and advanced
-
challenges and contribute to publications in high-impact scientific journals. Requirements: Education: PhD in Physics, Materials Science, Nanotechnology, Electrical Engineering, or a closely related field at