JADS: PhD on Software Testing for AI Software (O,8 – 1,0 FTE)

Updated: 2 months ago
Job Type: Temporary
Deadline: 21 Oct 2022

Jheronimus Academy of Data Science (JADS) Den Bosch, isproud to start with three large Robust AI labs together with:

  • Deloitte (Auditing for Responsible AI Software Systems) - 5 PhD's
  • DPG Media (Responsible Media Lab) - 5 PhD's together with University of Amsterdam (UvA)
  • LaNubia (Innovation Lab for Utilities on Sustainable Technology and Renewable Energy) - 5 PhD's
  • JADS is seeking enthusiastic colleagues for the position of PhD students. We operationalize the huge ambition around AI by explicitly aligning our research agenda on Robust AI with the United Nation's sustainable development goals.
    The project is funded in a public-private partnership by NWO/NLAIC and the private partners. This position is part of the Deloitte project.

    Short Description

    The next generation of enterprise applications is quickly becoming AI-enabled, providing novel functionalities with unprecedented levels of automation and intelligence. As we recover, reopen, and rebuild, it is time to rethink the importance of trust. At no time has it been more tested or valued in leaders and each other. Trust is the basis for connection. Trust is all-encompassing: physical, emotional, digital, financial, and ethical. A nice-to-have is now a must-have; a principle is now a catalyst; a value is now invaluable.

    Are you an enthusiastic and ambitious researcher with a completed master's degree in a field related to machine learning (Computer science, AI, Data Science) or in Electrical Engineering with an affinity for AI and deep learning? Does the idea of working on real-world problems and with industry partners excite you? Are you passionate about using trustworthy AI methods for the next generation of auditing processes, which are increasingly AI-enabled and data-driven? And are you interested in delivering new tools to support testing of the next generation of AI software?

    We are recruiting a Ph.D. candidate who will develop and validate novel concepts, methods, and tools for monitoring, auditing, and fostering the testability of AI software systems and trial them with industrial partners who work with Deloitte.

    Job Description

    This vacancy falls under the auspices of the JADE lab, which is the data/AI engineering and governance research UNIT of the JADS, and DELOITTE. In particular, this position is associated with JADE's ROBUST program on Auditing for Responsible AI Software System (SAFE-GUARD), which is financed under the NWO LTP funding scheme with Deloitte as the key industry partner.

    Whilst the overall objective of SAFE-GUARD is auditing of AI software, it may be further refined in the following more elaborated goal: "Explore, develop and validate novel auditing theories, tools, and methodologies that will be able to monitor and audit whether AI applications adhere in terms of fairness (no bias), explainability, and transparency (easy to explain), robustness and reliability (delivering same results under various execution environments), respect of privacy (respecting GDPR), and safety and security (with no vulnerabilities)."

    The industrial setting of the deep involvement of Deloitte will balance the rigor with relevance and ascertain fit with societal requirements and trends, validation with industrial case studies.

    Scientific Challenge

    Quality Assurance (QA) is a core part of any technology or business delivery, which is also true in the context of AI methodologies. This process can be done not only by assessing whether the method used, i.e., ML technique, works; but also by considering its tuning parameters and, finally, the quality and the completeness of the data exploited. This 'testing' activity is essential to deliver first and monitor an AI product that should always be available, performant, and reliable. In particular, this research project aims at security-by-design using generative adversarial learning. It will apply AI and search-based techniques to generate test cases that simulate more successful attacks against running software. The result will be a series of techniques, patterns, and finally, a tool to equip the next generation of auditors and DevOps AI engineers with machinery to help ascertain their AI software successfully implements security-by-design principles.


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