Job
- Level
- Experienced
- Job Field
- IT, Data, Security
- Employment Type
- Part Time/Full Time
- Contract Type
- Temporary employment
- Location
- Bremerhaven
- Working Model
- Onsite
Job Summary
In this role, you will develop AI-based transfer learning approaches for processing hydroacoustic sensor data and evaluate the robustness of machine learning models in maritime applications.
Job Technologies
Your role in the team
- As a new team member in the Group for Situation Awareness and Cybersecurity within the Department of Maritime Security Technologies, you will explore and develop innovative AI-based approaches to data processing in the underwater domain, focusing on the transferability of machine learning models between different hydroacoustic sensors (e.g., side-scan sonar, multibeam echo sounder, side-scan sonar).
- You plan and execute research projects on the development and evaluation of transfer learning strategies to enhance the efficiency and robustness of AI models, taking into account the heterogeneity of sensor data.
- This includes the design of shared feature backbones, the application of domain adaptation and multi-task learning, as well as the preprocessing of raw data using GANs or specialized filters.
- You conduct experimental investigations, analyze results using appropriate metrics and visualizations, derive new research questions from the findings, and test the work in an experimental maritime situational awareness system.
- You actively contribute to the development of flexible, adaptable, and secure security solutions for maritime infrastructures – with a direct link between fundamental research and practical application.
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Our expectations of you
Education
- Completed academic university degree (Master's / Diploma) in Computer Science, Electrical Engineering, Mathematics, Physics, or another relevant field.
Qualifications
- In-depth knowledge of Deep Learning (especially CNNs, Transformers, Autoencoders) and their application to time- and space-structured data.
- Knowledge in data fusion, multisensor integration, and cross-modal architectures.
Experience
- Practical experience with Transfer Learning, Domain Adaptation, and evaluation in complex domains.
- Programming skills in Python and C, as well as experience with PyTorch or TensorFlow.
- Experience with Docker, CI/CD, computer networks, and Linux systems. Good command of English and the ability to communicate complex research findings clearly.
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What we offer
- The remuneration is in accordance with the applicable collective agreements of the public service (federal level).
- DLR stands for diversity, appreciation, and equality of all people.
- We promote autonomous working and the individual development of our employees in their personal and professional environment.
- Our numerous training and development opportunities are available to you for this purpose.
- Equal opportunity is a particular concern for us; therefore, we especially aim to increase the proportion of women in science and leadership.
- We give preference to applications from severely disabled people if they are professionally qualified.
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Benefits
Work-Life-Integration
Topics that you deal with on the job
Job Locations
This is your employer
Deutsches Zentrum für Luft- und Raumfahrt eV.
As one of the leading research centers in the field of aerospace in Germany, DLR offers its approximately 8,700 employees the unique diversity of topics in aviation, spaceflight, energy, traffic, security and digitalization.
Description
- Company Size
- 250+ Employees
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Agriculture, Silviculture
Dev Reviews
by devworkplaces.com
Total
(1 Review)3.4
Workingconditions
4.2Career Growth
2.8Engineering
3.1Culture
3.5
Computer Scientist - Transfer Learning
- Location
- Bremerhaven
- Working Model
- Onsite
- Diversity
- Open for all genders
