Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Monash University
- ETH Zurich
- University of Colorado
- University of Sheffield
- University of Bristol
- University of Glasgow
- Heriot Watt University
- Inria, the French national research institute for the digital sciences
- SUNY Polytechnic Institute
- University of Nebraska–Lincoln
- University of North Carolina at Chapel Hill
- University of Toronto
- HHMI
- Imperial College London;
- KTH Royal Institute of Technology
- National Centre for Nuclear Research
- Nature Careers
- Oregon State University
- Rice University
- Simons Foundation/Flatiron Institute
- Stony Brook University
- Technical University of Munich
- The University of Chicago
- Tilburg University
- Tilburg University; 16 Oct ’25 published
- University Paul Sabatier
- University of A Coruña
- University of British Columbia
- University of California San Francisco
- University of California, Berkeley
- University of Exeter;
- University of Nebraska Lincoln
- University of Texas at Austin
- University of Vienna
- University of Warsaw
- University of Washington
- Université d'Orléans
- Zintellect
- 28 more »
- « less
-
Field
-
through the atmosphere. These models will be used, in Bayesian inference frameworks, to estimate surface fluxes from in situ and satellite observations. The derived emissions are used to track progress
-
the quality of uncertainty estimates by standard methods for LLMs, particularly with deep generative models, and (iii) develop a benchmark for uncertainty quantification in LLM-based scientific agents. The main
-
that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying
-
, Bayesian inference, and decoding/encoding methods. (Optional) Conduct in vivo calcium imaging experiments in C. elegans to study how neural circuits generate behavior. Engage in creative, hypothesis-driven
-
, custom-trained neural networks, and related tools. Analyze and interpret high-dimensional neural datasets using systems neuroscience approaches such as neural networks, Bayesian inference, and decoding
-
multi-dimensional niche models, and applying advanced Bayesian spatio-temporal methods. You will: Build n-dimensional abiotic niches for >6,700 species and estimate population positions within them
-
of the research include: (1) Designing and executing methods to integrate data from different sources, including developing a Bayesian Hierarchical Modeling framework; (2) using integrative modeling approaches
-
-ever Immune Digital Twin – a personalizable computer replica of the immune system – to enable everyone and anyone to assess and optimize the health of their immune system and simulate and predict its
-
” between data and models, including likelihood-free inference (e.g. Approximate Bayesian Computation) and simulationbased calibration, to ensure the ABMs remain predictive and falsifiable rather than
-
personalizable computer replica of the immune system – to enable everyone and anyone to assess and optimize the health of their immune system and simulate and predict its future ability to respond to diseases. Why