Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- SciLifeLab
- University of Oslo
- CNRS
- International PhD Programme (IPP) Mainz
- Ludwig-Maximilians-Universität München •
- NTNU - Norwegian University of Science and Technology
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- University of Cambridge;
- University of Exeter
- University of Warwick;
- 1 more »
- « less
-
Field
-
of treatments for a specific disease or condition can vary across individuals, so that in settings where multiple treatment options are available, different individuals may require different treatments to obtain
-
communities across different lake habitats; (ii) evaluate the consequences of biofilm compositional changes for the production and trophic transfer of health-promoting biomolecules (i.e., essential
-
-for-time field survey approach to (i) assess the effects of lake oligotrophication and changing winter conditions on benthic biofilm communities across different lake habitats; (ii) evaluate
-
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
-
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
-
of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine learning in general. OCBE
-
about uncertainty processing across these different scales. At later stages of the project, the role holder will have the opportunity to work with co-Is responsible for climate modelling and longitudinal
-
cancer, inflammatory diseases, and complex infections. This PhD project aims to develop a new generation of multifunctional polymeric nanocarriers using Polymerisation-Induced Self-Assembly (PISA). Unlike
-
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