Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Aalborg University
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial
- The University of Manchester
- University of Oslo
- CNRS
- Chalmers University of Technology
- DIFFER
- Ecoles Pratique des Hautes Etudes - PSL
- 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
- NTNU Norwegian University of Science and Technology
- National University of Singapore
- Norwegian University of Life Sciences (NMBU)
- Queensland University of Technology
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- SciLifeLab
- University of Birmingham
- University of Bristol
- University of Cambridge
- University of Cambridge;
- University of Exeter
- University of Surrey
- University of Texas at El Paso
- University of Warwick;
- Uppsala universitet
- 18 more »
- « less
-
Field
-
the best outcome. Precision medicine methods leverage individual-level characteristics to help optimise treatment choices for individuals. This project will leverage recent advances in Bayesian statistical
-
uncertainty about those conclusions (uncertainty quantification). Bayesian inference and experimental design methods are increasingly used in scientific practice, and offer appealing theoretical guarantees when
-
before the deadline. In many applications such as biological sciences, social science, and engineering, we encounter high-dimensional observations. Bayesian approach can provide a flexible modeling
-
Approximation calculations, whose direct use in Bayesian parameter estimation is currently computationally prohibitive. By providing a fast and statistically controlled surrogate for these calculations
-
are essential, along with the ability to build and extend statistical pipelines. An interest in Bayesian inference applied to biology is also important. A background in computational proteomics or LC-MS/MS
-
incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
-
integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
-
reinforcement learning (RL), active learning, Bayesian decision theory, and stochastic optimisation for partially observed and evolving systems. Key research directions include: adaptive data acquisition
-
integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
-
, including reinforcement learning, hierarchical models, Bayesian inference etc. More details of key responsibilities of this role, in addition to the essential and desirable job criteria, are available in