Sort by
Refine Your Search
-
Country
-
Employer
- Aalborg University
- SciLifeLab
- The University of Manchester
- University of Oslo
- Abertay University
- CNRS
- Constructor Knowledge Labs gGmbH
- Helmholtz-Zentrum Berlin für Materialien und Energie
- Inria, the French national research institute for the digital sciences
- International PhD Programme (IPP) Mainz
- Ludwig-Maximilians-Universität München •
- NTNU - Norwegian University of Science and Technology
- Norwegian University of Life Sciences (NMBU)
- Queensland University of Technology
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- University of Birmingham
- University of Bristol
- University of Cambridge
- University of Exeter
- University of Surrey
- University of Warwick;
- Uppsala universitet
- 13 more »
- « less
-
Field
-
proficiency in oral and written English, creativity, thoroughness, and a structured approach to problem-solving Additional qualifications Experience with one or more of the following areas is meriting: Bayesian
-
mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
-
, modify and extend. The project will explore a range of AI predictive and generative methods, such as large language and vision models (LLMs/VLMs), inverse procedural modelling, Bayesian optimisation, world
-
data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
-
to accelerate formulation discovery. Experimental data will be organised into a comprehensive database and analysed using statistical learning and Bayesian optimisation, establishing a closed-loop framework
-
Experiments (DoE) and Bayesian optimization, manage research data using the NOMAD research data infrastructure, and apply data-driven optimization strategies. Analyze and interpret experimental data
-
, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
-
Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
approach is based on neural techniques known as SBI (Simulation-Based Inference) [Cranmer et al., 2020]. SBI enables the resolution of inverse problems using generative AI methods and Bayesian statistics
-
, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
-
existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas