Sort by
Refine Your Search
-
Category
-
Country
-
Program
-
Employer
- University of Oslo
- EPFL
- University of New South Wales
- Delft University of Technology (TU Delft)
- Monash University
- NTNU - Norwegian University of Science and Technology
- SciLifeLab
- University of Glasgow
- University of Sheffield
- Aarhus University
- City of Hope
- Macquarie University
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- 3IA Côte d'Azur
- Aarhus University (AU)
- Brandenburgische Technische Universität Cottbus
- CNRS
- Center for Drug Evaluation and Research (CDER)
- Cornell University
- Duke University
- ECE - Paris - Ecole d'ingénieurs
- European Molecular Biology Laboratory (EMBL)
- Fraunhofer-Gesellschaft
- Ghent University
- INAF-Osservatorio Astronomico di Trieste
- INSERM
- Imperial College London
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- International PhD Programme (IPP) Mainz
- Kent State University
- Ludwig-Maximilians-Universität München •
- Michigan State University
- Nagaoka University of Technology
- Natural Resources Institute Finland (Luke)
- Oak Ridge National Laboratory
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- Sandia National Laboratories
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- Technische Universität München
- The University of Queensland
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- Umeå University
- University of Agder
- University of Cambridge;
- University of Exeter
- University of Lund
- University of Nottingham
- University of Oxford
- University of Pennsylvania
- University of Turku
- University of Vienna
- University of Warwick;
- Université de Bordeaux / University of Bordeaux
- Yale University
- 45 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
-
(NSSD). In this role, you will conduct fundamental research into the integration of Bayesian methodologies with system dynamics modeling, advancing statistical methods and the open-source scientific
-
Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
-
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
-
or a closely related discipline. Knowledge of genetics and genomics and a passion for applying quantitative approaches to biological and medical research questions. Strong expertise in (Bayesian
-
-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
-
. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
-
. Understanding what variation persists, how populations evolve, and why responses differ among populations is important both for explaining diversity in nature and for predicting the evolutionary consequences
-
incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
-
to test competing hypotheses for how neuroblastoma evolves, such as collateral phylogenetic branching versus convergent evolution. Each hypothesis can be simulated under different mechanistic assumptions