Sort by
Refine Your Search
-
Country
-
Employer
- Nature Careers
- Northeastern University
- Oak Ridge National Laboratory
- Argonne
- CEA
- Free University of Berlin
- Indiana University
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- Stanford University
- Technical University of Denmark
- UNIVERSITY OF HELSINKI
- UNIVERSITY OF VIENNA
- University of Cincinnati
- 3 more »
- « less
-
Field
-
, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular
-
the group’s research and philosophy head over to www.d2r2group.com Qualifications Strong background in ab-initio calculations of materials (density functional theory) and high-performance computing
-
. Develop and apply ab initio computations, molecular dynamics simulations, and machine learning models. Collaborate with other researchers within the group and external partners. Present research findings
-
by time of appointment. The proposed research will leverage multiple computational many-body techniques (including classical and quantum Monte Carlo, molecular dynamics, and ab initio methods) and
-
analysis with X-ray and entron diffraction. Property characterisation using a physical property measurement system (PPMS) and a SQUID magnetometer (MPMS). Ab-initio DFT calculations for property predication
-
simulation (classical and ab-initio molecular dynamics, DFT, simulations) Proficiency in programming languages like Python, C++, or Fortran for custom analysis tools. Experience in developing workflows
-
properties and electrical characterization will be carried out. The results will be compared with ab initio calculations and will provide input for physical models based on real devices to predict key metrics