586 parallel-computing-numerical-methods-"Simons-Foundation" positions at University of Sheffield
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
with a high degree of accuracy and attention to detail and will have previously supported student or programme related processes. They will also be able to build effective relationships with staff and
-
Award at the University of Sheffield. Modern engineering relies heavily on computational models that predict how structures behave under real operating conditions. These models simulate how assets such as
-
an accident. This contrasts to current safety methods that identify known hazards in advance, and monitor for their occurrence - instead, the approach relies on identifying the bounds of known safe behaviours
-
. These models will incorporate: Analytical approximations for complex biological systems Finite-element methods for solving partial differential equations Stress-strain balance calculations Mass-transfer
-
, high-speed parallel processing of powdered feedstock benefits from precise control over material microstructure and offers improved part functionality in a wide range of applications. Students will have
-
by exploring a novel therapeutic angle that could overcome the limitations of current anti-inflammatory drugs. How will you do this? You’ll be trained to use state-of-the-art methods like live imaging
-
profile to vaccine responsiveness Understand which individuals benefit from “anti-ageing” supplements or medications How you will do this: You will be trained to use cutting-edge rapid imaging methods
-
of recognition: auditory, olfactory and social cues, coupled with powerful analytical methods to investigate how individuals decide who to socially interact with. In this project, we will conduct field
-
. This project will utilise a combination of molecular and cell biology approaches, microbiology and advanced quantitative proteomic methods. ZDHHC5 knockout cells will be used to study the perturbed cell surface
-
local environment, but current imaging and transcriptomic resources are largely descriptive snapshots. This project will deliver a computational framework that integrates lineage-resolved, snapshot time