Description

ASSISTANT/ASSOCIATE/FULL PROFESSOR
Job Summary
We are seeking candidates with expertise in Strong AI, as broadly described above. The following is a non-exhaustive sample of research areas that align well with our interests.

1) Neural-symbolic AI: Symbolic AI operates on structured data, knowledge graphs, physics theorems, rules, and logic, enabling explicit reasoning and the manipulation of abstract concepts based on prior human knowledge. Neural-symbolic AI integrates the learning capabilities of neural networks with the reasoning and representational abilities of symbolic AI, thereby enabling more complex reasoning and problem-solving that meet real-world expectations.

2) Human-in-the-loop Machine Learning: The ideal candidate will bring innovative approaches to designing interactive AI systems that leverage human intuition and expertise to guide the learning process, improve data annotation, and refine model decisions. The ideal candidate is also expected to explore the critical interplay between AI technologies and ethical considerations, particularly the moral, societal, and legal implications of AI, such as bias, privacy, accountability, and the impact of AI decisions on individuals and communities.

3) Probabilistic Modeling Toward Strong AI: We are seeking candidates whose expertise is grounded in a sophisticated understanding of how probabilistic modeling plays a crucial role in knowledge-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden Markov models, high-dimensional probability, stochastic modeling, and other relevant ideas and techniques.

4) Explainability: This area focuses on unraveling the complexity of AI models to make their decisions understandable and transparent to both experts and non-experts alike. The ideal candidate will possess a deep expertise in developing innovative techniques and methodologies for explainable AI, including but not limited to, feature attribution methods, counterfactual explanations, visualization techniques, and interpretable machine learning models. We are also interested in AI-enabled approaches in STEM that provide transparent, citation-driven justification for synthetic inferences and for the scientific or engineering conclusions they support.

5) Secure AI: This area emphasizes the convergence of AI technologies and cybersecurity practices. The candidate will focus on designing and implementing cutting-edge security measures to protect AI technologies from adversarial attacks, data breaches, and other vulnerabilities, ensuring that AI systems are resilient against evolving threats. Their work should emphasize not only the theoretical underpinnings of AI security but also practical applications.

6) AI and Education: This area of focus aims to equip the next generation of IT professionals and workforce at large with the knowledge and skills necessary for the responsible application and creation of AI technologies. A significant aspect is to extend AI education beyond traditional student populations to encompass the broader society, thereby enhancing public understanding of AI’s potential and challenges. The successful candidate will demonstrate a strong track record in curriculum development, workforce development, educational program evaluation, and outreach activities.

Requirements

Minimum requirements for this position include:
(1) A Ph.D. or equivalent in one of the disciplines noted above or a related discipline that aligns with a focus on research in Strong AI.
(2) A strong publication record or potential in the field of expertise.
(3) A strong research program with existing external funding or potential for funding.
(4) Commitment to quality teaching at the graduate and undergraduate levels.