Sort by
Refine Your Search
-
Listed
-
Country
-
Program
-
Employer
- UNIVERSITY OF SOUTHAMPTON
- University of Oslo
- Aalborg University
- CNRS
- Indiana University
- Montana State University
- National University of Singapore
- Nature Careers
- University of Amsterdam (UvA)
- Aalborg Universitet
- Cornell University
- Inria, the French national research institute for the digital sciences
- Institut de Físiques d'Altes Energies (IFAE)
- Johns Hopkins University
- KTH Royal Institute of Technology
- Lawrence Berkeley National Laboratory
- Massachusetts Institute of Technology (MIT)
- Max Planck Institute for Physics, Garching
- Newcastle University;
- Queen's University
- RIKEN
- Technical University of Denmark
- The University of Southampton
- UNIVERSITY OF HELSINKI
- Universidad Politécnica de Madrid
- University of Amsterdam (UvA); Published yesterday
- University of Amsterdam (UvA); yesterday published
- University of Bristol
- University of Bristol;
- University of Leeds
- University of Leeds;
- University of London
- University of Manchester
- University of Massachusetts Medical School
- University of Michigan
- University of Minnesota
- University of Oxford
- University of Southampton;
- 28 more »
- « less
-
Field
-
-ray Magnetic Circular Dichroism (XMCD), X-ray imaging or resonant magnetic scattering. Demonstrated ML experience (e.g., dimensionality reduction, spectral unmixing, Bayesian inference, or physics
-
spectrographs with various spectral resolutions, operating from 0.5 to 28 µm. Our group has developed the Bayesian modeling tool FORMOSA (Petrus et al. 2023). It allows the inference of low-resolution (R = λ/Δλ
-
to address more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
-
and conducting climate model simulations and analysing large volumes of ESM simulations. Knowledge of reduced-order modelling and Bayesian inference is highly valued, and experience with climate
-
University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 1 month ago
modalities Developing and applying polygenic risk scores and causal inference models to predict disease onset, progression, and treatment response Implementing machine learning and statistical genetics
-
measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
-
Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
-
are essential, particularly in one or more of the following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph
-
experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
-
, 2022) and extended this to the triple equivalence between neural dynamics, Bayesian inference, and algorithmic computation (Commun Phys, 2025). -We validated it within in vitro neural networks (Nature