Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nanyang Technological University
- National University of Singapore
- RMIT UNIVERSITY
- Simons Foundation/Flatiron Institute
- University of Nottingham
- University of Oslo
- Zintellect
- CNRS
- CRANFIELD UNIVERSITY
- Eisbach Bio GmbH
- FCiências.ID
- Hiroshima University
- Hokkaido University
- Nature Careers
- Paul Scherrer Institut Villigen
- REQUIMTE - Rede de Quimica e Tecnologia
- RMIT University
- SciLifeLab
- Silesian University of Technology
- Universidade de Coimbra
- University of Nottingham;
- University of Stavanger
- University of Tartu
- 13 more »
- « less
-
Field
-
Simulation group to apply classical Molecular Dynamics and Machine Learning approaches for development of a new class of hybrid polyphenol-lipid nanoparticles with tuneable internal structure and exploration
-
research experience will also be considered. Preferred skills: Experience in infectious disease and animal models Experience performing virologic techniques, molecular biologic laboratory assays, cell
-
, molecular dynamics, and machine learning, to model battery electrolyte and solid electrolyte interphase (SEI), while collaborating with experimentalists. Qualifications • Ph.D. in Computational Materials
-
, Physics, Computer Science, or a related field. Hands-on experience with computational materials methods (e.g., DFT, molecular dynamics, machine learning force field simulations). Proficiency in Python
-
Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT's Materials Modelling and Simulation group to apply classical Molecular Dynamics and Machine Learning approaches
-
at the RMIT Melbourne CBD campus About the Role We are seeking a Postdoctoral Research Fellow to join RMIT’s Materials Modelling and Simulation group to apply classical Molecular Dynamics and Machine Learning
-
integrated research in computational, information and experimental sciences. (1) Development of molecular dynamics simulation model of network formation by free radical polymerization in an extension
-
vegetables. Model bacterial pathogenic levels through complex survival and growth patterns. Utilize either data analysis or molecular analysis tools (or both) to inform strategies to optimize pre-harvest
-
the area of fundamental modelling with molecular dynamic and ice/substrate interface bonding analysis. The main aspects of the research fellow role include conducting icing experiments in the Cranfield Icing
-
invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology