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
-
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
-
for learning about models from data, 2) incorporation of expert knowledge in model building through Bayesian prior elicitation, and 3) develop new methods for identification of conflicts in different parts
-
, XRF, isotopic, and tephra analysis, alongside the construction of Bayesian age–depth models using radiocarbon, 210Pb, and tephrochronology. Candidates with experience in metagenomics (sedimentary aDNA
-
postdoctoral research associate to work with Professor Michael Desai at Harvard University on projects involving inferring sequence-function landscapes, using a combination of empirical data and ML methods (e.g
-
characterization (SEM, TEM, XPS, FTIR, etc.) Programming skills (Python, MATLAB, or similar) Exposure to machine learning methods (e.g., Bayesian optimization, active learning) is an advantage Understanding
-
text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference
-
scientific narratives Interest in microbiome science and women's health Preferred but not required: Experience with microbiome or multi-omic data analysis, causal inference methods, or infectious disease
-
, XRF, isotopic, and tephra analysis, alongside the construction of Bayesian age–depth models using radiocarbon, 210Pb, and tephrochronology. Candidates with experience in metagenomics (sedimentary aDNA
-
Technology. Applicants should hold a doctoral degree in public health, epidemiology, or a related discipline, and have strong experience in longitudinal data analysis and advanced causal inference methods (e.g
-
: Experience with microbiome or multi-omic data analysis, causal inference methods, or infectious disease epidemiology Why This Role Impact: Contribute to research informing the prevention of urogenital