2. Quantum machine learning for remote sensing


Gabriele Cavallaro

The research group ‘‘High Productivity Data Processing (HPDP)’’ at the Jülich Supercomputing Centre (JSC) is highly active in developing parallel and scalable machine (deep) learning algorithms for remote sensing data processing, medical research and retail sectors. By being located at JSC, HPDP can rely on state of the art High Performance Computing (HPC) technologies and innovative quantum computing systems.

What is the data science project’s research question? Can quantum computers enhance the computational performance of machine learning algorithms when solving remote sensing problems?

What data will be worked on?  Open remote sensing datasets

What tasks will this project involve?  Programming in Python

What makes this project interesting to work on?   Access to disruptive computing technologies

What is the expected outcome?   Contribution to research paper, Contribution to software development

What infrastructure, programs and tools will be used? Can they be used remotely?  HPC systems and D-Wave Quantum Annealer (QA) computers. They can be used remotely. 

What skills are necessary for this project?   Data analytics / statistics, Scientific computation, Data mining / Machine learning, Deep learning, High-performance computing

Is the data open source? Yes

Interested candidates should be at Postdoc-level.  Gabriele Cavallaro is looking for 1 visiting scientist, working on the project together with the team.