Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- Technical University of Denmark
- University of Oslo
- Technical University of Munich
- Utrecht University
- ;
- BRGM
- Bielefeld University
- Centrale Supelec
- Chalmers University of Technology
- DAAD
- Ecole Centrale de Lyon
- FCiências.ID
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- French National Research Institute for Agriculture, Food and Environment (INRAE)
- INRIA
- Inria, the French national research institute for the digital sciences
- Linköping University
- Ludwig-Maximilians-Universität München •
- Maastricht University (UM)
- Nature Careers
- Newcastle University
- Radboud University
- Swedish University of Agricultural Sciences
- Technical University Of Denmark
- The University of Manchester
- UCL
- Umeå University
- University of Amsterdam (UvA)
- University of Bologna
- University of Surrey
- University of Texas at El Paso
- Uppsala universitet
- Vrije Universiteit Amsterdam (VU)
- 25 more »
- « less
-
Field
-
Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and
-
. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential
-
embedded in the CCSS community. Nice to have Experience with viral genomics, phylogenetics, Bayesian or likelihood-based inference, infectious disease modeling, or high-performance computing. Additional
-
Experience with viral genomics, phylogenetics, Bayesian or likelihood-based inference, infectious disease modeling, or high-performance computing. Our offer a position for 18 months, with an extension to a
-
modelling: -Weighted PINNs, -Bayesian PINNs, -Stochastic PINNs, -Ensemble PINNs, -Domain-decomposition PINNs. Selected approaches will be tested within a dedicated data-assimilation framework
-
stochastic modeling, Bayesian inference, data fusion and modern machine learning. Its research activities span various application domains such as security, non-destructive testing, infrared imaging and
-
stimulated luminescence (OSL) dating of sediments and rocks, palaeoseismology, megaliths, Bayesian chronological modelling, archaeoseismicity, stable continental regions (SCR), Armorican Massif. Context and
-
-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
-
complemented with a supersonic module grounded on the work of Bufi & Cinnella (link to the research paper). In a second step, using Bayesian processes (Lam et al., 2015) and new acquisition functions
-
. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models