Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- University of Oslo
- National University of Singapore
- Zintellect
- NTNU - Norwegian University of Science and Technology
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Center for Drug Evaluation and Research (CDER)
- City of Hope
- Cornell University
- Indiana University
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- King Abdullah University of Science and Technology
- King's College London
- Max Planck Institutes
- Nanyang Technological University
- Sejong University
- The University of Queensland
- UNIVERSITY OF MELBOURNE
- University of Birmingham
- University of Michigan
- University of Nottingham
- University of Otago
- 11 more »
- « less
-
Field
-
, recurrent memory, Bayesian modelling, uncertainty quantification and machine learning systems. Emphasis will be on methods that design and implement new architectures for (auto-regressive) sequence modelling
-
, internationally connected research programme spanning Bayesian infectious disease modelling, AI-driven epidemic forecasting, genomic epidemiology, and pandemic preparedness. The postholder will work with Asst. Prof
-
Experience in one or more of the following areas is preferred: Statistical genetics Human genetics Population genetics Evolutionary genetics Bayesian statistics Machine learning Large-scale genomic data
-
agent based/individual based modelling, SEIR modelling, geospatial statistics, Bayesian statistics, burden mapping, measuring the impact of the environment on disease among others. The PI has projects in
-
PK/PD studies, Bayesian model-based dose-finding approaches, adaptive designs and master protocols, including basket and umbrella trials. You will be expected to build productive collaborations across
-
Experience with population genetics or statistical genetics Familiarity with Bayesian methods, probabilistic modeling, or graphical models Experience with scientific computing in Python, JAX, Torch, Julia, C
-
Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
-
department by carrying out both quantum information and computation projects ranging from quantum device characterization, error mitigation/suppression/correction, Bayesian-inference-based quantum information
-
(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
-
project . Fluent oral and written communication skills in English Background in biomarker analysis and/or compound specific isotope analysis and/or archaeometric dating techniques and Bayesian statistics