Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- University of Oslo
- Harvard University
- National University of Singapore
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Zintellect
- Nanyang Technological University
- University of Maryland, Baltimore
- University of Nottingham
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Center for Drug Evaluation and Research (CDER)
- Cornell University
- Deakin University
- Florida Atlantic University
- Indiana University
- Max Planck Institute for Political and Social Science, Göttingen
- Montana State University
- Nature Careers
- Trinity College Dublin
- UNIVERSITY OF MELBOURNE
- UNIVERSITY OF NOTTINGHAM NINGBO CHINA
- University of Birmingham
- University of Sydney
- University of Texas at Austin
- ;
- Amgen
- Auburn University
- Barnard College
- Carnegie Mellon University
- Center for Biologics Evaluation and Research (CBER)
- Centro de Engenharia Biológica da Universidade do Minho
- Curtin University
- Georgia Institute of Technology
- Hokkaido University
- Institute of Computer Science CAS
- King Abdullah University of Science and Technology
- London School of Hygiene & Tropical Medicine;
- QUEENS UNIVERSITY BELFAST
- Queen's University
- Queen's University Belfast
- Queen's University Belfast;
- Scuola Superiore Sant'Anna di Pisa
- Simula UiB
- Swansea University
- The University of Queensland
- The University of Southampton
- UCL;
- University College London
- University of Basel
- University of British Columbia
- University of California, San Francisco
- University of Leeds
- University of London
- University of Michigan
- University of Otago
- Université Libre de Bruxelles (ULB)
- Østfold University College
- 46 more »
- « less
-
Field
-
challenging for developing advanced spatiotemporal forecasting models. By integrating causal inference with interpretable AI techniques, the project aims not only to improve prediction accuracy but also to
-
) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts of complex models. BioM is an
-
getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
-
discovery, disease stratification, prognostic modelling, and causal inference. Work closely with clinician-scientists to translate computational findings into clinically relevant insights and high-impact
-
methods. In the latter case, estimates and causal inferences will be made, and natural experiments will be analysed using econometric techniques. The project also includes dissemination activities and the
-
AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
fellows. Your involvement in this exciting field will include taking a lead role in developing and implementing new methods and software to improve phylogenetic inference. The focus will be on developing
-
subcellular mechanisms (proliferation, differentiation) with multicellular mechanical and biochemical interactions. Apply Advanced Statistical Methods: Perform Bayesian parameter estimation and identifiability
-
. Our main research areas are neuroimaging data analysis (fMRI & EEG, iEEG, anatomical and diffusion MRI), brain dynamics modelling, causality and information flow inference, nonlinearity and
-
background. The ideal candidate will have existing expertise in several of the following areas, aligned with our research focus: 1) Causal inference, invariant learning and representation learning
-
(longitudinal designs, moderation and mediation, causal inference), library research (Pubmed searches, systemic review methods), and statistical analysis (data visualization, descriptive analyses, time series