Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
. This is primarily for an NIH-funded project developing multimodal variational autoencoder models and probabilistic trajectory analyses for latent spaces formed from neural, genetic, and behavioral data
-
-FUTURE-Probabilistic Geospatial Machine Learning for Predicting Future Danish Land Use under Compound ClimateImpacts. The project is funded by Villum Foundation under the Villum Synergyscheme in which 2
-
Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
-
theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
-
. Beiglböck. The main research areas of the group include stochastic processes, mathematical finance, and probabilistic transport theory. Our ideal candidate already has experience with modern methods in
-
[map ] Subject Areas: https://careers.epfl.ch/job/Lausanne-Postdoc/1163457655/ Appl Deadline: 2025/11/01 11:59PM (posted 2025/10/09) Position Description: Apply Position Description A postdoc position
-
Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 month ago
fields including health, agriculture and ecology, sustainable development. More information, please visit https://team.inria.fr/scool/projects Odalric-Ambrym Maillard is a permanent researcher at Inria. He
-
probabilistic frameworks. Experience with machine learning or AI methods for localization or perception (e.g. learning-based SLAM, data-driven sensor fusion) is a plus. Underwater or field robotics experience
-
built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine
-
deviation analysis of probabilistic models, and associated problems in PDE, with emphasis on identifying both well- and ill-posed examples and the interplay between probabilistic analysis and the analysis