Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Argonne
- University of Oslo
- Nature Careers
- University of Minnesota
- CNRS
- Cornell University
- Indiana University
- King Abdullah University of Science and Technology
- Lunds universitet
- Massachusetts Institute of Technology
- McGill University
- Rutgers University
- University of Liverpool
- University of Oxford
- Dartmouth College
- Duke University
- Emory University
- GFZ Helmholtz-Zentrum für Geoforschung
- Ghent University
- ICN2
- Institut de Físiques d'Altes Energies (IFAE)
- Institutionen för akvatiska resurser
- Lehigh University
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- Montana State University
- North Carolina State University
- RIKEN
- Singapore-MIT Alliance for Research and Technology
- Swedish University of Agricultural Sciences
- Technical University of Denmark
- The University of Arizona
- Tilburg University
- Umeå University
- Umeå universitet
- University of Cambridge;
- University of Connecticut
- University of Idaho
- University of Liverpool;
- University of Miami
- University of Oxford;
- University of Tübingen
- University of Washington
- Utrecht University
- 33 more »
- « less
-
Field
-
computers lack such abilities. The goal of the Adaptive Bayesian Intelligence Team is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with
-
models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available
-
guidance, navigation, and control (GNC) systems. The successful candidate will develop and validate Bayesian and non-Gaussian estimation algorithms, data assimilation methods, and tracking frameworks
-
prompted by the same environmental stressors across a species’ geographic range and through time. The post holder will develop a new Bayesian model, MESS, to analyse the dynamics of extirpation. The MESS
-
environmental stressors across a species’ geographic range and through time. The post holder will develop a new Bayesian model, MESS, to analyse the dynamics of extirpation. The MESS model will adapt
-
on cognitive diagnostic assessments and student learning in introductory STEM courses, with a focus on Bayesian quantitative methods and mixed-methods education research. Position Number 4C1852 Department
-
FieldAstronomyYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in astrophysics or a related field. - Experience in data analysis. - Proficiency in Bayesian statistics and nonparametric
-
in R or Python Desired - evidence of strong computational skills and large dataset analysis - experience with hierarchical Bayesian modeling - expert knowledge of plant functional ecology - fluency in
-
: Experience with novel or high-throughput characterization methods; accelerated stress testing and stability evaluation of thin-film photovoltaics; and data-driven experimental optimization, including Bayesian
-
. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration