Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- CNRS
- NIST
- Nature Careers
- DAAD
- IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava
- Aarhus University
- CEA Grenoble
- CNR Istituto Nanoscienze (NANO)
- CNRS / Sorbonne Université
- Carnegie Mellon University
- Chalmers University of Technology
- ETH Zürich
- European Magnetism Association EMA
- Hanyang University
- Harvard University
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Heriot Watt University
- Istituto SPIN del Consiglio Nazionale delle Ricerche
- Max Planck Institute for Sustainable Materials •
- NTNU Norwegian University of Science and Technology
- Tor Vergata University of Rome
- UCL
- Univ. Lorraine CNRS
- University of Leeds
- University of Luxembourg
- University of North Carolina at Chapel Hill
- University of South Carolina
- University of Texas at Dallas
- University of Washington
- Université Caen Normandie
- Université de Lille
- Université de Rouen Normandie
- 22 more »
- « less
-
Field
-
materials, including porous dimension, building ingredients, wettability, etc., and their interactions with water and gas species include CO2, Hydrogen and methane. Using atomistic modeling, the study will
-
quasicrystals, glasses, and other materials. Applicants must hold a Ph.D. in Physics or equivalent and must be an expert in the structure and thermodynamics of quasicrystals, glass, and other complex atomistic
-
properties in solid-state plasmonic systems in the presence of disorder" Where to apply Website https://selezionionline.cnr.it Requirements Additional Information Eligibility criteria PhD in physics, chemistry
-
latitudes and deep convection in marine and continental environments). You will work closely with colleagues at Leeds and Warwick (who are developing and validating the toy/atomistic models) to translate
-
. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing
-
remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful
-
systems (LIED, Université Paris Cité), experts in classical atomistic simulations (molecular dynamics and coarse-graining, LMCE, CEA/DAM/DIF), as well as specialists in continuum simulations (finite element
-
how nanoparticles prefer to attach to each other. The machine-learning models will be validated against detailed atomistic simulations and compared with experimental results on self-assembly. Ultimately
-
discipline Apply: https://mgician.eu/research/doctoral-candidate-projects/dc3/ DC4: Atomistic Modelling and Design of Thermoelectric Materials and Interfaces Host: King’s College London (KCL), London
-
Basse Normandie. PHD title: Doctorat de Physique PHD Country: France Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/137565 Requirements Specific Requirements Applicant