Research Fellow In AI For Malware Detection On (IoT) Devices

The post holder will perform research in AI for Internet of Things (IoT) malware detection. This will involve gaining a deep understanding of how AI systems can be applied to design IoT malware detectors. The key requirements are to design deep neural network (DNN) malware detection solutions that generalise across multiple platforms and can run efficiently on low-resource embedded IoT hardware.

The post holder will also undertake high-quality and novel research in deep-learning-based Internet of Things (IoT) malware detection. This will involve developing deep-neural-network-based malware detectors that use multi-task learning for improved cross-platform robustness, then adapting these networks to run on low-resource IoT devices using novel pruning and distillation techniques.

About the person:

To be shortlisted for interview, candidates must demonstrate that they meet the following criteria:

• Have or be about to obtain a PhD in engineering or physical sciences area.
• Evidence of recent high-quality research experience in machine learning/deep learning or cybersecurity, or both, as evidenced by a strong track record of publications in leading journals and conferences in relevant areas.
• Demonstrable proficiency in a high-level programming language such as Python, C++ etc.
• Demonstrable ability to design, train and test machine learning/AI systems using appropriate methodologies and datasets.

To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information on our website

This post is available for 36 months. Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.

What we offer:

Beyond a competitive salary, the University offers an attractive benefits package including a holiday entitlement of up to 8.4 weeks a year, pension schemes and development opportunities. We support staff wellbeing with flexible working options, work-life balance initiatives and support for physical and mental health. You can find more detail on all of this and more at www.qub.ac.uk/directorates/HumanResources/pay-reward-and-benefits.

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