Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
, making a new model which is suitable for a variety of polymer systems. This will involve integrating molecular dynamics simulations, electronic structure calculations, and machine learning techniques
-
, to industrial catalysis and green energy production. The aim of this PhD project is to study hydrogen colliding with surfaces at a fundamental, molecular level to gain unprecedented insight into the role
-
into interfacial thermal transport. The goals are to: run ab-initio molecular simulations to sample relevant nanomaterial/liquid interfaces. construct new MLPs by using generated data from 1. and validate them. use
-
, we will employ molecular simulations and theoretical methods to understand how to design materials for hydrogen-energy applications. With these design principles, we aim to develop, synthesize, and
-
necessary for the benefit of the project; • employ simulations and data analysis routines to analyze your data; • help to establish a scientifically outstanding and warmly communicative
-
. 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
-
an enthusiastic research assistant to take part in research investigating the molecular causes of ALS using human iPSC-derived motor neurones. You will use CRISPR-Cas9 editing to generate new cell lines harbouring
-
, to industrial catalysis and green energy production. The aim of this PhD project is to study hydrogen colliding with surfaces at a fundamental, molecular level to gain unprecedented insight into the role
-
PhD Studentship - Discovery of Novel Biomarkers in Rare Neuromuscular Disease Using Stem Cell Models
) investigate the potential of stem-cell derived neuromuscular constructs to identify and measure in vitro biomarkers of rare NMD (ii) use omics technologies to help characterise rare NMD at a molecular level to
-
including pristine and defective Li2O, LiOH, LiH and their major surfaces. The simulations will be used to train machine-learned forced fields (MLFFs) to explore hydrogen diffusion using molecular-dynamic (MD