Phd Thesis scalable Process Patterns and Decision Frameworks for Trustworthy Ai Engineering

As Austria's largest research and technology organisation for applied research, we have set ourselves the goal of making substantial contributions to solving the major challenges of our time: climate change and digitalisation. With a high standard of excellence, we are working to make a substantial contribution to the competitiveness and resilience of the economy and society in Europe. The **AIT AI TaskForce** is a strategic initiative aimed at fostering systematic, interdisciplinary collaboration and providing infrastructure for enhancing AI research and operations. By integrating expertise across competence units and research centers, the AI TaskForce supports AIT's goal of embedding robust, ethical, and transparent AI practices across all applied research activities. The PhD project will actively contribute to this strategic initiative by researching and developing scalable, practical frameworks for implementing Trustworthy AI principles in diverse projects and across multiple technology readiness levels. Additionally, your work will directly support the **FAIR-AI** flagship project for the Austrian Artificial Intelligence Mission AIM 2030, ensuring your research generalises and gains high visibility and meaningful interaction with national and European stakeholders from academia, industry, and public institutions. Aligned closely with the **AI Factory Austria (AI:AT)**— a collaboration between **AIT**and **Advanced Computing Austria (ACA)** — you will leverage state-of-the-art infrastructure, interdisciplinary know-how, and national networks. **Your results will**enhance the AI Factory’s Austria capability to rapidly implement innovative, trustworthy AI solutions in cooperation with diverse partners, thereby** significantly strengthening AIT’s leading role in shaping responsible AI technologies and practices for Austria and beyond**. For our location in **Vienna**, we are offering a: PHD THESIS "SCALABLE PROCESS PATTERNS AND DECISION FRAMEWORKS FOR TRUSTWORTHY AI ENGINEERING" AI Taskforce - **Generalisable Requirements and Patterns for Trustworthy AI**: Develop scalable frameworks, guidelines, checklists, and decision trees to systematically embed Trustworthy AI (TAI) principles such as explainability, transparency, fairness, accountability, and data governance - into diverse software and research contexts. - **Value-Based Software Engineering (VBSE) for Trustworthy AI**: Integrate explicit stakeholder-value elicitation, structured empirical validation, and traceable decision logging into AI software engineering processes to ensure transparency and accountability in design and development decisions. - **Adaptive Process Models for Multi-Modal and Multi-Disciplinary AI Systems**: Create adaptable, modular software management processes and semantic-integration techniques tailored for handling diverse data modalities (e.g., text, images, audio, sensor data) and supporting interoperability across multiple domains and stakeholders. - **Empirical Validation Continuous Monitoring of Trustworthy AI**: Develop robust empirical validation methodologies (metrics, pilot evaluations, stakeholder feedback loops) and continuous-monitoring dashboards to systematically assess and demonstrate AI systems' compliance with TAI principles over their entire lifecycle. - **Governance Models and Roles for Trustworthy AI**: Define effective governance structures, roles, and responsibilities (e.g., AI ethics committees, semantic knowledge hubs, compliance officers) that support multi-level, cross-functional management of TAI activities within research and innovation-driven organisations. - **Scalable Templates and Decision Aids for Applied Research Environments**:Design and evaluate practical, scalable templates, artifact patterns, and decision support tools specifically optimised for flexibility, ease of use, and broad applicability in diverse applied research settings. - **Semantic Integration with Blueprints and Patterns**: Develop semantic-integration approaches, including domain-specific blueprints, reusable patterns, and controlled vocabularies, to ensure clarity, interoperability, and traceability of Trustworthy AI requirements across disciplines and stakeholder groups. **Your qualifications**: - Master's degree in Business Informatics, Computer Science, Software Engineering, Artificial Intelligence, Data Science, or related fields - Experience or strong interest in software process modelling, business process modeling, software requirements engineering, or software quality management - Familiarity with Trustworthy AI principles (e.g., transparency, explainability, fairness, accountability) and related regulatory frameworks (e.g., EU AI Act, GDPR) is highly beneficial - Practical knowledge or experience with programming languages (e.g., Python, R), data analysis, machine learning frameworks, or software engineering tools is advantageous - Interest and willingness to publish scientific results in international journa