Computer Scientist - Real-time Data Fusion

C/C++ CI/CD (Continuous Integration/Delivery) CMake Docker

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At the Institute for the Protection of Maritime Infrastructure in Bremerhaven, we research and develop innovative solutions to strengthen the resilience of maritime infrastructure and make it adaptable, safe and sustainable. Working closely with partners from research, industry and other maritime safety stakeholders, we combine technological innovation with practical expertise and offer you the chance to work on pioneering projects.

What you can expect

As a researcher in the Situational Awareness and Cybersecurity Group within the Department of Maritime Security Technologies, you will research and develop innovative algorithms for fusing radar data with other sensor data to create a robust, real-time and threat-adaptive situational picture in the maritime domain. You will develop and test methods for synchronised data fusion, quality assessment, weighting based on reliability and relevance, and the detection and correction of inconsistencies. You will place particular emphasis on resilience against interference and attacks (e.g. jamming, spoofing). The work is carried out in close cooperation with partners from research and industry. The results are directly incorporated into combined situational awareness systems and tested in real-world scenarios - with the aim of enabling early protective measures and sustainably strengthening the security of maritime infrastructure., * Designing and carrying out research on the fusion of radar data, vessel movement and other information to detect security anomalies in the maritime domain

• Development and implementation of algorithms for anomaly detection in time series and geospatial data, in particular using unsupervised learning, autoencoders and active learning
• Carrying out data preparation and fusion from heterogeneous sources, including temporal and spatial alignment as well as data quality control
• Validation of the models using datasets from field experiments and long-term measurements
• Publication of results at international conferences

• A completed academic degree (master's / university diploma) in Computer Science, Software Engineering or another relevant field
• Good knowledge of machine learning, particularly unsupervised learning, anomaly detection, time series analysis and their application to sensor data
• Experience in developing efficient algorithms and data structures for data-intensive real-time applications
• Practical experience in sensor data fusion, the analysis of large, heterogeneous datasets and the integration of geodata and time series
• Programming skills in C++ and Python, as well as experience with data structures, algorithms, software architectures and development tools (e.g. CMake, Git, Docker, CI/CD)
• Good written and spoken English, as well as the ability to communicate complex research findings clearly and precisely