Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- KINGS COLLEGE LONDON
- University of Washington
- Aarhus University
- Empa
- Heriot Watt University
- King Abdullah University of Science and Technology
- Purdue University
- Technical University of Denmark
- UNIVERSITY OF HELSINKI
- University of Oxford
- ASNR
- Argonne
- Arizona State University
- Aston University
- CEA
- City University London
- Columbia University
- Duke University
- Eindhoven University of Technology (TU/e)
- Embry-Riddle Aeronautical University
- Florida International University
- Friedrich Schiller University Jena
- Georgetown University
- Japan Agency for Marine-Earth Science and Technology
- KTH Royal Institute of Technology
- King's College London
- Max Planck Institute for Multidisciplinary Sciences, Göttingen
- Nature Careers
- Oak Ridge National Laboratory
- Rutgers University
- Stony Brook University
- The Ohio State University
- UNIVERSITY OF VIENNA
- University of Adelaide
- University of Central Florida
- University of Colorado
- University of Florida
- University of Idaho
- University of London
- University of Maine
- University of Minnesota
- University of Oslo
- University of Sydney
- University of Virginia
- Université de Caen Normandie
- Virginia Tech
- 37 more »
- « less
-
Field
-
mathematical background, including expertise in stochastic optimization (e.g. Markov decision theory and dynamic programming) and applied probability (Bayesian statistics). Excellent coding skills (e.g., in Java
-
objectives. For example, digital image processing tools, such as filtering or mathematical morphology, could be evaluated to extract structural elements of road edges from images. By combining spectral and/or
-
: developing and testing new approaches to water resources modelling, application of Bayesian inference methods to environmental problems, machine learning and data science applications, undertaking analysis and
-
engineering aspects as well as filtering and signal processing. The work is linked to a series of VINNOVA funded projects, REDO, REDO2 and CORD. The purpose of the research is to understand how different
-
to implement advanced computational pipelines, including machine learning, deep learning, Bayesian inference, and probabilistic mixed membership modeling for innovative research. · Contribute
-
(2024). 2. Multiresonant Grating to replace Transparent Conductive Oxide Electrode for bias selected filtering of infrared photoresponse, Tung H. Dang, M. Cavallo, A. Khalili, C. Dabard, E. Bossavit, H
-
not limited to, QC filtering, enrichment profiling, sequence content comparison, data tracing, and graphical representation. - Large-scale in vitro production and characterization of mRNA. The most
-
in collaboration with international research and industrial partners. The position requires software development within the topics of navigation, sensor fusion, Kalman filtering and gravity field
-
techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
-
-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing