ScAI invites postdocs applications in all areas of artificial intelligence, where AI is interpreted broadly to include all of traditional AI, machine learning and data science. Subareas of interest include (but are not limited to):
- Mathematical Foundations of AI (optimization for ML, Markov chains, random walks and Martingales, matrix and tensor factorization for AI, learning theory, etc.),
- Machine Learning (deep learning, reinforcement learning, probabilistic models, green ML, neuro-symbolic ML, quantum ML, etc.),
- Data Science (data mining, graph mining, data streams, information retrieval, etc.),
- Traditional AI (search, constraints, knowledge representation and reasoning, game theory, multi-agent systems, planning, reasoning under uncertainty, etc.),
- Applied AI (natural language processing and text mining, computational biology, computer vision, speech processing, robotics, physical AI, knowledge graphs, humans and AI, cognitive systems, neuroscience, IoT and sensor networks, AI on the edge, cloud-based AI, etc.),
- Applications of AI to domain areas (healthcare, material science, education, energy, atmospheric science, bioinformatics, communication, agriculture, transportation, e-commerce, finance, crowdsourcing, unmanned vehicles and aerospace, industry 4.0, intelligent and sustainable buildings and infrastructure, hardware, social impact, other emergent applications of AI).
ScAI strongly encourages applicants with demonstrated track-record of working at the intersection of an application area and the AI fields.
ScAI post-doctoral fellows may have done a PhD in any relevant area such as computer science, electrical engineering or mathematics, or also any application area such as civil engineering, mechanical engineering, management or medicine. Irrespective of the specific area of PhD, the candidate must have performed high quality research in their area, and must have decent exposure to AI techniques.