Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- University of Oslo
- National University of Singapore
- Center for Drug Evaluation and Research (CDER)
- NTNU - Norwegian University of Science and Technology
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Zintellect
- City of Hope
- 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 Agder
- University of Birmingham
- University of Michigan
- University of Otago
- 10 more »
- « less
-
Field
-
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
-
datasets. Statistics and mathematics Strong grounding in multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference
-
anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and
-
Machine Learning Seminar Group Advanced Tutorial Lecture Series on Machine Learning Non-Parametric Bayes Tutorial Course (October 9, 16 and 28, 2008) Bayesian statistics in other labs Machine Learning and
-
Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
-
qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage. Experience with land-surface models
-
modelling analyses, including differential gene expression analysis, microbiome diversity analyses, host–microbiome association testing, metagenome-wide association analyses (mGWAS), hierarchical Bayesian
-
Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
-
processes of the study systems of our collaborators. Core components of the research involve, among others, Bayesian hierarchical modelling, shrinkage methods, machine learning (ML) or dimension reduction
-
Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | about 21 hours ago
well as Bayesian borrowing can help address sample size and ethical concerns for rare diseases. Collaboration between the statistician at DB9 and the clinical team at the Division of non-malignant hematology to map