This doctoral project is part of a collaborative research project with several partners, including the Jülich Research Center. The goal of the project is to research and develop new algorithms and methods in the field of QML and their application to machine learning tasks in the automotive industry. QML combines the principles of quantum physics with machine learning techniques to potentially achieve advantages over classical machine learning.
The doctorate will be carried out by Saarland University, in cooperation with the Jülich Research Centre.
Your tasks include:
- The extension, development and analysis of new quantum machine learning algorithms for current and near-future quantum computers
- The investigation of the application of quantum machine learning methods to concrete problems from the automotive industry
- Implementing and testing the algorithmic concepts with real data using QML software libraries
- The evaluation of the latest technological and algorithmic developments for current and near-future quantum computers
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PhD Student in Quantum Machine Learning
Aim and Benefits of PhD Student in Quantum Machine Learning
- Food allowances
- Employee cell phone possible
- Employee discounts possible
- Employee participation possible
- Employee events
- Coaching
- Flexible working hours possible
- Hybrid working possible
- Health measures
- Company pension scheme
- Mobility offers
Requirements for PhD Student in Quantum Machine Learning Qualification
- Master's degree in physics, computer science, mathematics or comparable fields
- Knowledge of quantum algorithms required
- Knowledge of machine learning required
- Ideally experience in programming and software development
- Confident written and spoken English language skills
- Ability to work independently in interdisciplinary teams
- Analytical thinking and strategic working methods
Application Deadline
Not SpecifiedHow to Apply
Interested and qualified? Go to
Mercedes-Benz on tas-daimler.taleo.net to apply
For more deails, visit Mercedes-Benz scholarship webpage.