JOB DESCRIPTION

With the advancement in quantum computers (QC), quantum machine learning has emerged as a promising and exciting field of research that aims to leverage the power of QC to solve complex problems in machine learning.
Vibration data is one of the most informative response of a system that is being used extensively for monitoring purposes such as non-destructive testing (NDT), structural health monitoring (SHM). However, extracting useful information from vibration signals is not a trivial task.

The main goal of the Postdoc project is to develop novel quantum machine learning algorithms for vibration-data analysis for SHM/NDT applications. The project will involve both theoretical and experimental aspects, such as:

• Developing quantum algorithms for data preprocessing, feature extraction, dimensionality reduction, classification, and regression on vibration signals.
• Implementing and testing the quantum algorithms on state-of-the-art quantum hardware and software platforms
• Comparing the performance and scalability of the quantum algorithms with classical machine learning methods on real-world vibration datasets from aerospace structures and materials.
• Exploring the potential advantages and limitations of quantum machine learning for SHM/NDT problems, such as noise robustness, computational speedup.

REQUIREMENTS

The ideal candidate should have:

• A PhD degree in quantum computing, quantum information, quantum algorithms or relevant topics.
• A good knowledge and experience in machine learning, data analysis, signal processing, and optimization.
• A strong expertise in programming languages such as Python, MATLAB, etc. and familiarity with quantum computing frameworks such as Qiskit, Cirq, etc.
• A good command of English (written and spoken) and excellent communication skills.
• A high level of creativity, initiative, independence, and teamwork spirit.

Please refer the Job description for details