The Machine Learning and Knowledge Discovery Group of INESC-ID is looking for qualified applicants for three fully funded PhD student positions on topics related with the application of deep learning techniques to problems with societal impact. These positions are funded by a large-scale research project in responsible AI, supported by the Resiliency and Recovery Facility. The successful candidates will pursue a PhD degree in Computer Science and Engineering at Instituto Superior Técnico, in Lisbon Portugal. The broad topics of research are: 1 - Normalization of geolocation records using deep learning techniques, 2 - High confidence information retrieval and question answering, 3 - Application of reinforcement learning methods to the generation of efficient algorithms.

We are currently looking for a university assistant (full-time doctoral student for up to 4 years) for the Computational Data Analytics group of Prof. Johannes Fürnkranz. We are particularly interested in researchers who will strengthen our expertise in one or more of the following areas: Machine Learning and Game Playing, Symbolic Machine Learning, Machine Learning and Logic, Inductive Rule Learning, Interpretable AI, Data Mining and Knowledge Discovery.

At Sony AI, we are searching for a (Senior) Research Scientist Data Mining/Knowledge Discovery & ML to join one of our offices in Barcelona (preferred), Zurich or Tokyo. The role involves working with a highly diverse, international team of scientists and engineers pushing the boundaries of AI/ML research.

Knowledge discovery with computers requires a huge amount of search. Analogical reasoning is effective for efficient knowledge discovery. Therefore, we proposed analogical reasoning systems based on first-order predicate logic using Neuro-Symbolic AI. Neuro-Symbolic AI is a combination of Symbolic AI and artificial neural networks and has features that are easy for human interpretation and robust against data ambiguity and errors. We have implemented analogical reasoning systems by Neuro-symbolic AI models with word embedding which can represent similarity between words. Using the proposed systems, we efficiently extracted unknown rules from knowledge bases described in Prolog. The proposed method is the first case of analogical reasoning based on the first-order predicate logic using deep learning.