Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- University of Oslo
- Utrecht University
- Monash University
- University of Cambridge
- Aalborg University
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- Amsterdam UMC
- CNRS
- Curtin University
- DAAD
- ENVT INRAE
- Eindhoven University of Technology (TU/e)
- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
- King's College London
- King's College London;
- Maastricht University (UM)
- Max Planck Institute for Molecular Genetics •
- Max Planck Institute of Molecular Plant Physiology •
- Molde University College
- Nature Careers
- Northeastern University London
- SciLifeLab
- Swedish University of Agricultural Sciences
- UNIVERSIDAD EUROPEA
- Ulm University •
- Umeå University
- Universite de Montpellier
- University of Birmingham
- University of Birmingham;
- University of East Anglia;
- University of Exeter;
- University of Göttingen •
- University of Münster •
- University of Newcastle
- University of South-Eastern Norway
- University of Southern Denmark
- University of Twente
- Université Paris Cité
- Uppsala universitet
- 31 more »
- « less
-
Field
-
Degree or equivalent Skills/Qualifications Essential Strong background in applied mathematics, mathematical biology, or computational modelling. Experience with partial differential equations (PDEs) and
-
grounding and interpretability. This PhD project aims to develop a novel, mathematically principled generative modeling framework for discrete sequence data by unifying diffusion-based generative modeling
-
to develop a novel, mathematically principled generative modeling framework for discrete sequence data by unifying diffusion-based generative modeling with coalescent theory from population genetics
-
of neuroscience and new computational methods, combining mathematical and biophysical models with state-of-the-art diffusion and functional MRI data, in typically and atypically developing populations through
-
: formulate and analyze stochastic models of evolving populations using methods from statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions
-
they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale
-
a lack of a coherent and mathematically rigorous methodology for how health, environmental and population exposure and vulnerability data can be combined to optimally issue warnings in order to
-
candidate will: Hold an MSc (or equivalent) in Physics, Mathematics, Computer Science, or related fields Have strong computational and modelling skills, including proficiency in Python and experience with
-
—to macroscopic, population-level observables in rapidly evolving pathogens such as SARS-CoV-2 and influenza. Concretely, you will: formulate and analyze stochastic models of evolving populations using methods from
-
of pathogens, their interactions with hosts and the environment, and how they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modelling of pathogen