Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Delft University of Technology (TU Delft)
- CNRS
- Justus Liebig University Giessen •
- Monash University
- The University of Manchester
- Utrecht University
- Eindhoven University of Technology (TU/e)
- Fondazione Bruno Kessler
- Maastricht University (UM)
- University Medical Center Utrecht (UMC Utrecht)
- University of Nottingham
- University of Warwick
- AALTO UNIVERSITY
- Cranfield University
- Curtin University
- ETH Zürich
- European Magnetism Association EMA
- Forschungszentrum Jülich
- Grenoble INP - Institute of Engineering
- Heidelberg University
- Inria, the French national research institute for the digital sciences
- Institute of Neuroscience and physiology, Sahlgrenska Academy, university of Gothenburg
- Institute of materials and machine mechanics Slovak academy of sciences
- Karl Landsteiner University of Health Sciences
- Karolinska Institutet, doctoral positions
- Linköping University
- NTNU - Norwegian University of Science and Technology
- Nicolaus Copernicus University
- Oak Ridge National Laboratory
- Politecnico di Milano
- Queensland University of Technology
- Technical University of Denmark (DTU)
- The University of Manchester;
- UNIVERSITY OF VIENNA
- University of Birmingham
- University of Cambridge
- University of East Anglia
- University of Graz
- University of Luxembourg
- University of Oxford
- University of Plymouth
- University of South Carolina
- University of Surrey
- University of Vienna
- Université Libre Bruxelles
- 35 more »
- « less
-
Field
-
Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 28 days ago
to address these challenges is DNA-based data storage, which offers several advantages, including extremely high data density, long-term retention, and low energy consumption [2]. From a density perspective
-
reduced maintenance demands which are key for the broader adoption of offshore wind energy. However, several challenges must be addressed, such as the machine's relatively low power density and power factor
-
dynamics, and density functional theory (DFT) can be combined to accelerate the discovery and design of next-generation organic semiconductor materials with tailored optoelectronic properties. The project
-
industrial and transport applications still rely on combustion-based technologies due to their need for high energy densities and thermal process requirements. A promising solution lies in electrofuels (e
-
standard simulation methods. This PhD topic falls within the area of computational materials science and will involve the use of established methods such as density functional theory and molecular dynamics
-
behaviour and electrically tunable interfaces in 2D heterostructures. Methods include density functional theory (DFT), atomistic simulation, high-performance computing, and machine-learning-assisted materials
-
, for green hydrogen production, highly efficient metal halide perovskite-based photovoltaics and, of course, high energy density and safer batteries for e-mobility . You will work in a highly collaborative
-
activities will include: o Human Brain Modeling: Generating and maintaining patient-stratified iPSC lines and innovative 3D "brain chimeroid" models. o Advanced Neurophysiology: Assessing functional network
-
collaboration with Osaka University, where in situ kinetic measurement experiments can be conducted. Finally, this thesis is part of the ANR MUSiCAL project, a collaborative initiative between SIMaP (Université
-
training in first-principles electronic-structure and excited-state methods (density functional theory and the GW and Bethe-Salpeter-Equation approaches), machine learning for atomistic simulation, and high