Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- Technical University of Munich
- Westlake University
- Aarhus University
- Leibniz
- Massachusetts Institute of Technology (MIT)
- NEW YORK UNIVERSITY ABU DHABI
- Northeastern University
- The Ohio State University
- Tsinghua University
- UNIVERSITY OF VIENNA
- University of British Columbia
- University of Oxford
- University of Southern Denmark
- University of Vienna
- WIAS Berlin
- 6 more »
- « less
-
Field
-
approaches (e.g. ODE/PDE-Modelling, Agent Based Modelling) Experience in mathematical analysis of data-driven methods Solid experience in programming (R, Matlab, Python, etc.) Competence in working in inter
-
functional inequalities Rough paths, stochastic differential equations and stochastic PDEs The positions are full-time, fixed-term appointments, with an earliest start date on February 1st 2026. Attractive
-
are looking for: a highly motivated researcher with at least 3 years of research experience (possibly as part of the PhD) in a field related to mathematical analysis of partial differential equations (PDEs
-
surrogate models that approximate complex physical and biological systems traditionally modeled by PDEs or other computationally expensive simulations. By incorporating physical priors such as conservation
-
Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
: PhD in nuclear engineering, applied physics, computational science, or a closely related field; demonstrated experience in Multiphysics modelling, numerical methods for PDEs, and code verification and
-
or expect to hold a Ph.D. degree in Mathematics. 2) Have a solid background in one of the following fields: Kahler geometry, geometric PDEs (Ricci flow, harmonic map, Hermitian-Yang-Mills connection
-
measure theory and o-minimal geometry; • Ill-posed PDEs such as div(v)=F; • Non-absolutely convergent integration theories; • Radon-Nikodymification of geometric measures. Job Description Position
-
simulations of PDEs, deep learning, neural networks. Our research interest: Our focus is on theoretical and computational biological physics, ranging from the study of molecules to cells. We strive to leverage
-
supervision of PI Calina Copos and will engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical systems, and/or ML-based
-
join a research project on the Fokker-Planck equation—also known as the Kolmogorov forward equation— which is a fundamental Partial Differential Equation (PDE) that captures the time evolution