Machine learning software engineer állás, munka

• Develop, execute, and maintain test plans and test cases.
• Define, document and maintain SW tests according to software requirements
• Develop manual & automated software test procedure and test plans to verify software functionality, reliability, and performance.

• Have end-to-end responsibility, from data exploration to applying state-of-the-art machine learning techniques and building production-ready applications.
• Your work will directly enable innovative solutions in financial crime prevention and credit risk assessment.

• You will support on extracting, loading and processing (cleaning and transforming) complex data across different industries
• You will use your technical expertise to develop, test and deploy ML models based

• A dynamic and interdisciplinary research environment
• Hands-on experience with EEG and fNIRS data acquisition and lab equipment
• Exposure to state-of-the-art methods in neural signal processing and data curation

The rapidly growing Forensic department is currently seeking the right candidates for an excellent career opportunity in the Forensic Technology and eDiscovery team....

• Flexible starting date all year round and a permanent employment contract
• Individual program length and design your program in agile product areas, corporate start-ups or classic line organizations with a wide range of workshops and trainings

With us, you have the opportunity to perfectly combine your studies with practical experience while actively contributing to exciting projects. This allows you to gain valuable skills, expand your network, and grow both professionally and personally. We are seeking passionate and talented students...

Develop a content-aware data compression method for Distributed Acoustic Sensing (DAS) data that dynamically adapts to the characteristics of the signal to maximize data reduction while preserving essential information required for downstream machine learning (ML) tasks....

• Assess processes to identify improvement and automation opportunities.
• Collaborate with stakeholders and cross-functional teams to define actionable solutions.
• Define the needs for application and data landscape to enable automation and AI integration.

• ongoing information on developments, discussions, and trends regarding the use of AI in research
• ongoing information on current and developing legal or compliance requirements
• ongoing development of the (AI) software portfolio provided to research and administration at the GIGA

• Collaborate on developing and implementation of post-hoc explainable AI models for computer vision models
• Exploring and benchmarking of XAI algorithms for vision language models
• Investigate potential of large language models for concept based XAI