Research Fellow, School of Computing
4 weeks ago
Interested applicants are invited to apply directly at the NUS Career Portal
Your application will be processed only if you apply via NUS Career Portal
We regret that only shortlisted candidates will be notified.
Job DescriptionThe National University of Singapore invites applications for the position of Research Fellow in the Department of Computer Science, School of Computing (SoC). SoC is strongly committed to research excellence in all its dimensions: Searching for fundamental results and insights in developing novel computational solutions to a wide range of applications, building largescale experimental systems, developing theories and policies for effective management of information systems in organizations, and improving the well-being of society. We seek to play an active role both internationally and locally in the core and emerging areas of Computer Science and Information Systems.
The Research Fellow will be responsible for working closely with the Principal Investigator on one or more projects.
Description
Fuzzing is a powerful technique for finding security vulnerabilities in systems and is used by corporations regularly. In this work, we will study the role of AI in cybersecurity - by aiding fuzzing techniques with large language models.
Tasks
- Conduct Research on AI-Aided Fuzzing: Lead research efforts to explore the integration of large language models (LLMs) with fuzzing techniques for identifying security vulnerabilities in systems. Design experiments and methodologies to evaluate the effectiveness of AI-aided fuzzing in comparison to traditional methods.
- Develop AI Models for Fuzzing Enhancement: Develop and implement AI models, including deep learning architectures, reinforcement learning algorithms, and natural language processing techniques, to enhance fuzzing capabilities. Train these models on relevant datasets to improve their ability to generate meaningful inputs for fuzzing.
- Collaborate with Interdisciplinary Teams: Collaborate with interdisciplinary teams comprising cybersecurity experts, AI researchers and software engineers to integrate AI-aided fuzzing techniques into existing cybersecurity frameworks. Communicate findings and insights effectively to team members and stakeholders.
- Publish Research Findings: Document research findings in academic publications, conference papers, and technical reports. Disseminate knowledge gained from the study of AI in cybersecurity, particularly in the context of fuzzing, to the broader research community through presentations and workshops.
- Ph.D. in Computer Science, Cybersecurity, or a related field: A terminal degree in a relevant field is essential to demonstrate the depth of knowledge and expertise required for conducting advanced research in AI and cybersecurity.
- Research Experience in Fuzzing and AI: Proven track record of conducting research in fuzzing techniques and artificial intelligence, with a focus on deep learning, natural language processing, or reinforcement learning. Experience in applying AI methods to cybersecurity problems is highly desirable.
- Publication Record: Demonstrated ability to publish research findings in reputable conferences and journals relevant to AI, cybersecurity, or software engineering. Strong publication record in top-tier venues indicates the ability to contribute novel insights to the field.
- Programming and Software Development Skills: Proficiency in programming languages such as Python, C/C++, or Java is necessary for developing and implementing AI algorithms and fuzzing tools. Experience with software development methodologies and version control systems is advantageous.
- Analytical and Problem-Solving Skills: Strong analytical skills and problem-solving abilities are essential for designing experiments, analyzing data, and deriving actionable insights from research findings. Experience with experimental design and statistical analysis techniques is beneficial.
- Communication and Collaboration Skills: Excellent written and verbal communication skills are required to convey complex ideas and technical concepts to both technical and non-technical audiences. Ability to collaborate effectively with interdisciplinary teams and communicate research findings clearly is crucial.
- Self-Motivation and Initiative: Demonstrated ability to work independently and take initiative in driving research projects forward. Proven track record of self-motivated learning and exploration of new methodologies and technologies in the field of AI and cybersecurity.
- Candidates who meet these qualifications and possess a strong passion for advancing the state-of-the-art in AI-aided cybersecurity, particularly in the domain of fuzzing, are encouraged to apply.
Only shortlisted candidates will be notified.
Tell employers what skills you have
Deep learning
Security
Reinforcement Learning
Natural Language Processing
Artificial Intelligence
Computer Science
Python
computer sciences
publication
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