This client situated in Eindhoven, incorporates the European Advanced Research Lab and Corporate Development activities for the international organization.. She is a China-based technology company who is leading the way to become the “Digital Brain of Intelligent Driving” with core businesses in HD map, high accuracy positioning and automotive-grade semiconductors for ADAS and autonomous driving.
Founded in 2002, the company is the market leader in navigation map, dynamic traffic information, navigation software development and state-of-the-art customized telematics solutions to both passenger and commercial vehicles.
Now, she is ushering in the age of autonomous driving with a comprehensive technology development strategy and laying the foundation to become one of the most trustworthy autonomous driving solution providers in the China market and beyond.
For the organization we are looking for a
Data Scientist automotive technologies
We are looking for a specialist to join our team. We work in projects that are related to Cybersecurity, Machine Learning and automotive embedded systems.
Your work will help improve the security of current and future automotive technologies, including in-vehicle systems protection, secure V2X communications, autonomous driving and privacy.
Your work will involve several Data Analytics & Machine Learning tasks, collaborating in a team of Cybersecurity experts. The main work topics will be applied research and development of Cybersecurity solutions that work with automotive embedded systems, cloud computing, and automotive Data Analysis.
We are looking for someone flexible, curious, willing to learn and to adapt to the team needs in each different project. An affinity with Cybersecurity is a plus.
Your abilities should include experience with most of the following:
- Big data projects using Hadoop and Spark
- Anomaly detection, few-shot learning, unsupervised learning, semi-supervised learning.
- Different approaches to work with very imbalanced data
- EDA feature selection, feature enrichment
- Privacy issues and how to address
- Machine Learning security problems: adversarial examples, data poisoning, recovering training data, etc. and ways to avoid them.
- Time-series analysis.
- Programming in Python and / or R
- Knowledge of at least one library (Theano, TF, Pytorch, MxNet etc)
What we expect
Required experience / knowledge / skills:
- Master in Computer Science, Mathematics, Data Science, Physics, Telecommunications.
- Strong ethics
- Analytical approach to problems
- Being able to read and summarize research papers in Machine Learning and apply them
- Leadership and a make-do, problem solving attitude
- Original thinking
- Presentation skills
- Research publications, patents, public projects are a plus