Optimal Experimental design is the task of optimally allocating resources to make data (yet to be collected under this design setup) as valuable as possible. In this project you'll work on sequential Bayesian inference using the Ensemble Kalman filter and derive a way of computationally optimise the design using ideas from optimal control.
Supervisors
Primary Supervisor: Philipp Wacker
Key qualifications and skills
Solid background in maths (differential equations, Lagrangian multipliers, optimisation) as well as stats (Bayesian inference), and coding (optimally Python)
Does the project come with funding
No - Student must be self-funded
Final date for receiving applications
Ongoing
How to apply
Apply by email to primary supervisor
Keywords
optimal control, optimal experimental design, filtering
