Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Employer
- Monash University
- Imperial College London
- ;
- UNIVERSITY OF HELSINKI
- CNRS
- KINGS COLLEGE LONDON
- University of Glasgow
- Aston University
- Columbia University
- ETH Zurich
- Forschungszentrum Jülich
- Heriot Watt University
- Institut Pasteur
- King's College London
- Nature Careers
- Rice University
- SUNY University at Buffalo
- University of Manchester
- University of Oslo
- Utrecht University
- ; University of Southampton
- AUSTRALIAN NATIONAL UNIVERSITY (ANU)
- Argonne
- Arizona State University
- Australian National University
- Binghamton University
- CEA
- Dalhousie University
- ETH Zürich
- FCiências.ID
- Freenome
- Johns Hopkins University
- King's College London;
- NIST
- New York University
- North Carolina State University
- Purdue University
- Syracuse University
- Technical University of Denmark
- The University of Arizona
- UCL;
- University of Adelaide
- University of California, Los Angeles
- University of Colorado
- University of Florida
- University of London
- University of Massachusetts
- University of Miami
- University of Michigan
- University of South Carolina
- University of Sydney
- University of Texas at Arlington
- University of Warsaw
- University of Warwick
- Université d'Orléans
- Univerzita Karlova (Charles University)
- Utrecht University; Utrecht
- 47 more »
- « less
-
Field
-
spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
-
to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
-
more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
-
carbon, water and energy states. The successful applicant will specifically support carbon and water cycle science, applications and process model innovations using CARDAMOM-based Bayesian inference
-
more information for a given experimental budget. Efficient active learning depends on the careful co-design of experiments and inference algorithms. You will explore topics such as how to elicit
-
motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
-
processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
-
experience) in epidemiology, mathematical modelling, or a closely related quantitative discipline. Strong skills in statistical inference and coding in R. Experience analysing epidemiological or infectious
-
models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
-
, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in identifying the parameters of PDEs, while on the other, newly emerging