Sort by
Refine Your Search
-
time series modelling, age-period-cohort modelling and structural equation modelling. Whilst applicants need not to be familiar with ALL of these, they ought to be familiar with ONE (and to state in
-
. Quantitative Analysis: Demonstrated ability to handle multimodal datasets, conduct statistical analysis, and apply predictive modeling and validation techniques. Research & Publication: Skilled at critically
-
neuroscience, cancer biology, extracellular vesicle biology and in vivo modelling. The project itself will combine expertise in neuroscience and cancer biology and has the potential to lead to findings which can
-
and identification of pathology specific biological targets for treatment options. Finally, we will integrate our engine exhaust exposure models as an exemplar IPF driver to understand mechanisms
-
comprising chemists with expertise in organic chemistry, reactor design and innovative process analytics, and engineers with skills in fluid modelling, Life Cycle Assessment and sustainability. You will
-
, models, techniques and methods. Ability to assess and organise resource requirements and deploy effectively. Ability to build relationships and collaborate with others, both internally and externally. Some
-
the environmental impacts of proposed processes and compare them with conventional alternatives. • Develop process models using industry-standard software (e.g., Aspen Plus, HYSYS, SimaPro, or equivalent
-
the environmental impacts of proposed processes and compare them with conventional alternatives. • Develop process models using industry-standard software (e.g., Aspen Plus, HYSYS, SimaPro, or equivalent
-
utilize preclinical tumour models, high dimensional techniques including spatial mass spectrometry and immunocytochemistry, alongside complementary state-of-the-art molecular biology approaches to identify
-
set-up, and data collection and analysis. - Have the ability to analyse and interpret data using appropriate statistical packages (e.g., conducting linear mixed effects models in R). - Have experience