Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
focuses on improving our understanding of multiple sclerosis disease progression and how treatment can impact progression. This work will focus on the unique Novartis Oxford MS (NO.MS) dataset, the largest
-
predictive analysis for Novartis Oxford collaboration for AI in medicine. The collaboration focuses on improving our understanding of multiple sclerosis disease progression and how treatment can impact
-
designs such as observational study, randomized clinical trial, adaptive randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis Work
-
randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis. · Develop as an educator by taking an active teaching role in POCUS and EBM
-
will focus on developing and applying Bayesian statistical models to investigate and predict biofouling patterns to enhance our understanding of how environmental factors and antifouling technologies
-
areas and foster the development of multiple research projects involving one or more international collaborations. Open Science : The selected candidate will fully commit to open science by strictly
-
plasma-material interactions in fusion energy systems. You will also advance knowledge of key AI methods such as deep learning, operator learning, and Bayesian optimization, and apply it to develop next
-
Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated
-
of biofouling processes in marine environments. This role will focus on developing and applying Bayesian statistical models to investigate and predict biofouling patterns to enhance our understanding of how
-
. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials