ADIA Lab - Senior Computational Scientist - Trustworthy AI

About the role

Requirements

• - Expand and co-lead a world-class research program in Trustworthy AI within ADIA Lab, advancing the scientific foundations of reliable, transparent, and responsible AI systems.
• - Drive cross-disciplinary research combining machine learning, statistics, causal inference, optimization, computational science, and domain expertise to address challenges in explainability, robustness, fairness, safety, governance, and accountability.
• - Develop novel methodologies for understanding, auditing, evaluating, and validating AI systems, including large language models, foundation models, and emerging AI architectures.
• - Conduct fundamental and applied research on AI interpretability, robustness to distribution shifts and adversarial attacks, uncertainty quantification, model monitoring, and trustworthy deployment in high-stakes environments.
• - Build and coordinate a strong, collaborative research team and network, with an emphasis on scientific leadership, mentoring, and strategic partnership development.
• - Collaborate with national and international research institutions, advocating a community science approach that contributes to advancements in Trustworthy AI.
• - Build partnerships with universities, laboratories, industry, and government organizations to initiate joint programs, funding proposals, and collaborative platforms focused on AI safety, governance, and responsible innovation.
• - Publish research results in leading scientific journals and conferences, contribute to white papers and policy-oriented reports, and represent ADIA Lab at major international scientific and industry events.
• - Develop open-source software, benchmark datasets, evaluation frameworks, and reproducible methodologies that advance the Trustworthy AI ecosystem.
• - Supervise and mentor junior scientists, postdoctoral fellows, and students, nurturing future leaders in AI research and innovation.
• - Play a central role in shaping, growing, and coordinating the Trustworthy AI program as a core pillar within ADIA Lab’s mission, ensuring long-term sustainability, scientific excellence, and global visibility.
• - Work closely with ADIA Lab Fellows and the Advisory Board to define the strategic direction of Trustworthy AI research and its application across ADIA Lab’s scientific domains
• - 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.
• Preferred 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.

Skills

• machine learning
• statistics
• causal inference
• optimization
• computational science
• domain expertise
• explainability
• robustness
• fairness
• safety
• governance
• accountability
• large language models
• foundation models
• AI architectures
• interpretability
• distribution shifts
• adversarial attacks
• uncertainty quantification
• model monitoring
• trustworthy deployment
• open-source software
• benchmark datasets
• evaluation frameworks
• reproducible methodologies