Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
-
using AI and machine learning in developing new tools for better management of TIC members transformer fleets. Guided by experienced academic staff and supported by Industry experts through the TIC
-
variable models (e.g., CLIP, GLIP, MaskCLIP). Knowledge of Transferability in Machine Learning is desirable. Knowledge in Active Learning is desirable. Programming skills and experience with dataset
-
collaboratively with colleagues from multidisciplinary disciplines Excellent time management and planning skills, with a commitment to delivery Strong background in machine learning and/or deep learning, and signal
-
including the application of artificial intelligence and machine learning. You will engage with industry, government, and research collaborators, fostering partnerships that deliver outcomes aligned with
-
skills, with demonstrated capacity to establish and achieve goals. Desirable Background in deep learning and computer vision. Demonstrated experience in engineering and cross-disciplinary team working
-
/facility Build new and/or improve existing research-focused engineering technologies, systems, prototypes, machines and/or processes Perform other duties as reasonably directed by the Research Facilities
-
of machine learning techniques for neuroimaging data (e.g., classification, clustering, or regression models). Experience with automating neuroimaging workflows (e.g., using nipype/ BIDS-apps, or similar tools
-
(collecting data, analysing data) desirable. Background working with clinical groups, patients or vulnerable populations. Application of machine learning techniques for neuroimaging data (e.g., classification
-
with single-cell omics (Level A). This research aims to develop new statistical and machine learning methods to integrate and analyse data from genome-wide association studies (GWAS) and single-cell