Hybrid quantum algorithms for partial differential equations
We are currently accepting applications for the above mentioned position. This is a unique opportunity for junior researchers with a recent PhD degree in Physics or related fields to join one of DIPC’s high-profile research teams.
The aim if this project is to explore new ideas in the field of quantum and quantum-inspired (tensor network) machine learning and optimization methods to develop hybrid algorithms (classical & quantum) to solve Partial Differential Equations (PDEs). This includes, among others, exploring and developing more efficient algorithms for fluid dynamics, among others, improving the overall performance in time and energy cost, as well as in accuracy. The hybrid algorithms will make use of different QPUs in combination with state of the art Tensor Network methods. The research will be carried in collaboration with Multiverse Computing, in the context of the CUCO project.
It's required a PhD in quantum computing or quantum-inspired numerical simulation methods.
Contract duration: 1 year (possibility to extend up to 3 years)
Target start date: 01/09/2023
Application deadline: 15/06/2023
Application email: firstname.lastname@example.org
Funds: This project has received funding from the MCIN program “Severo Ochoa”, under reference AEI/ CEX2018-000867-S.
Interested candidates should submit an updated CV and a brief statement of interest to the following application email below.
Reference letters are welcome but not indispensable.
The reference of the specific opening to which the candidate is applying should be stated in the subject line, and the application must be received before the application deadline.
Although candidates are welcome to contact the project supervisors to know further details about the proposed research activity, please be aware that the application will be evaluated only if it is submitted directly to the email address listed below as application email.