I will be spending Feb 15-March 17 at the Isaac Newton Institute, Cambride, UK, for a program on Stochastic Dynamical Systems in Biology: Numerical Methods and Applications. Big thanks to the organizers,Radek Erban (Oxford), David Holcman (ENS – Paris), Samuel Isaacson (Boston) and Konstantinos Zygalakis (Southampton) for organizing this amazing opportunity to gather many creative people in our field at the same place!
We are currently hiring a PhD student to work on the project From Single Cells to Tumors – Multiscale Simulation of Stochastic (multi)cellular Systems
Deadline for application: March 15.
Instructions for how to apply can be found here:
Instructions for application.
We have an opening for a PhD student in computational systems biology. Interested candidates are encouraged to contact me for more details.
Apply to the position here.
The Center for Applied Mathematics (CIM) in Uppsala are looking for up to 3 PhD students in applied mathematics. Within this call there is an opportunity to joint the group working on the project From cell-cell interactions to embryo development: Multiscale models and simulation in systems biology. This project is a collaboration with Carolina Wählby.
The division of Scientific Computing are looking for 3 PhD students in numerical analysis/scientific computing. Here, a variant of the above project more focused on the multiscale method development is also available: Stochastic simulation of gene expression: From individual to interacting cells.
We are also looking for a Postdoc in Scientific Computing. This is an open position where the candidate will formulate a research plan (within the areas of interests in the department).
Many biochemical network models display scale separation with respect to reaction rates and/or molecular copy numbers. Depending on the type of question under study, different models are best suited to simulate the system. For some parts of the system, a macroscopic model might be appropriate. For other parts, a mesoscopic model may provide additional insight into the models dynamics. In other cases, a microscopic model might be needed to capture the fine-grained features of the model. To efficiently simulate systems that have different requirements with respect to modeling levels, hybrid methods offer an attractive approach.