ADIA Lab - Senior Computational Scientist - Trustworthy AI
On-siteAbu Dhabi, Abu Dhabi, United Arab Emirates
Job Summary
Expand and co-lead a world-class research program in Trustworthy AI, driving cross-disciplinary research combining machine learning, statistics, causal inference, and domain expertise to address challenges in explainability, robustness, fairness, and safety. Develop novel methodologies for auditing, evaluating, and validating AI systems, including large language models and foundation models, while conducting fundamental research on interpretability, adversarial attacks, and uncertainty quantification. Build and coordinate a collaborative research team, mentoring junior scientists and postdoctoral fellows, and establish partnerships with universities, industry, and government organizations to initiate joint programs and funding proposals. Publish research results in leading scientific journals and conferences, represent the lab at international events, and develop open-source software, benchmark datasets, and evaluation frameworks. Work closely with ADIA Lab Fellows and the Advisory Board to define the strategic direction of the Trustworthy AI program, ensuring long-term sustainability and global visibility within the scientific community.
Required Qualifications
- PhD in Computer Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, Computational Science, or a related field
- Demonstrated ability to conduct and coordinate research, preferably with more than 5 years of post-PhD experience
Desired Qualifications
- Proven record of leading a research group, major research initiative, or multidisciplinary project in AI, machine learning, or data science
- Demonstrated expertise in one or more areas of Trustworthy AI, including explainability, interpretability, robustness, AI safety, fairness, causal inference, uncertainty quantification, model auditing, privacy, security, or AI governance
- Experience evaluating and deploying machine learning systems in high-stakes domains such as healthcare, finance, climate science, cybersecurity, public policy, or critical infrastructure
- Strong understanding of modern AI systems, including deep learning, foundation models, large language models, reinforcement learning, and agent-based AI systems
- Experience developing open-source software, benchmark datasets, evaluation methodologies, or reproducible research frameworks
- Familiarity with high-performance computing environments, large-scale AI training and inference systems, distributed computing, and cloud-based research platforms
- Proficiency in programming languages and frameworks such as Python, PyTorch, TensorFlow, JAX, R, or related tools, as well as Git, Linux, and scientific computing environments
- Strong publication record in leading venues such as NeurIPS, ICML, ICLR, AAAI, KDD, AISTATS, JMLR, Nature Machine Intelligence, or equivalent
- Excellent leadership, mentoring, communication, and collaboration skills
- Experience engaging with policymakers, industry partners, standards organizations, or international research collaborations is highly desirable
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