Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- SciLifeLab
- ; The University of Manchester
- DAAD
- Chalmers University of Technology
- Nature Careers
- UNIVERSITY OF HELSINKI
- Umeå University
- University of Copenhagen
- University of Groningen
- ;
- ; University of Warwick
- Aalborg University
- Ariel University
- Dresden University of Technology •
- NTNU - Norwegian University of Science and Technology
- National Institute for Bioprocessing Research and Training (NIBRT)
- Technical University of Denmark
- University of Münster •
- University of New Hampshire – Main Campus
- University of Oslo
- University of Twente
- Wageningen University & Research
- 12 more »
- « less
-
Field
-
, biophysics Machine learning and generative AI Molecular modeling and molecular dynamics simulations LNP formulation and characterisation including e.g. small angle scattering, microscopy, single particle
-
, computational modelling, bioinformatic analysis, and experimental vascular biology. Based in a dynamic translational research environment of data-driven life science, computational imaging, and vascular surgery
-
is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in
-
. Experience with molecular dynamics software such as LAMMS is desirable. Experience with molecular simulation software is beneficial. To apply please contact Dr Siperstein - flor.siperstein@manchester.ac.uk
-
in prokaryotic models. The successful applicant will primarily work on characterizing the role and dynamics of viral RNA-based components during infection. Furthermore, participation in additional
-
, carbohydrate-active enzymes, polymer chemistry, spectroscopic methods, material science, 3D-printing, statistical physics and molecular dynamic simulations, formulation technologies. Excellent communication
-
; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
-
modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
-
Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic modelling of materials and machine learning. Experience in atomistic modelling (molecular
-
thesis on a topic relevant to this project. Proven experience in molecular dynamics simulations of proteins or protein-ligand complexes is an advantage.You possess:- A successfully completed MSc degree in