Assistant Professors in Artificial Intelligence and Data Science (0.8 -1.0 FTE)

Join the Artificial Intelligence and Data Science division, the largest division of the department of Information and Computing Sciences. This division is experiencing rapid growth and we are currently looking for new colleagues to expand and complement our diverse and vibrant research groups.

As an Assistant Professor in our division, you will actively engage in research, teaching and supervision in the field of Artificial Intelligence and Data Science. You develop, coordinate, and teach courses in our BSc and/or MSc programmes, as well as supervise BSc/MSc students and PhD candidates. To develop new research collaborations and application areas, you will actively seek internal and external collaborations and acquire funding for research. You are also encouraged to publish in renowned scientific journals, newspapers, and appear in other media (e.g., radio).

To strengthen our team we are looking for colleagues who fit well with one of the research areas described below: 

Knowledge Representation and Reasoning: the design and development of safe and reliable AI systems require models, tools, and techniques to synthesise and verify such systems. You are an expert in the field of knowledge representation and reasoning with a background in formal systems, logics, and computations. Deep reinforcement learning: multiagent (deep) reinforcement learning techniques have been successfully applied to develop AI systems for cooperative or competitive tasks. With your expertise in this area you contribute to our research in the field of safe and efficient (multiagent) reinforcement learning. Explainable AI: AI systems should be able to explain their internal constructs, processes and decisions to humans in order to increase their transparency and trustworthiness. You are eager to make a valuable contribution to our research in this area, in view of your background in a topic such as the following: explainable-by-design; generation of explanations for interactions, norms, values, and decisions; and/or methods to analyse and assess explanations. Transfer learning: in many application fields, there is only limited well-annotated data available to learn specific tasks, while much more data is available for related tasks. This calls for either an approach with models that can learn effectively on little data, or for an approach whereby models are adapted from data-rich domains to data-poor domains. You bring expertise in the development and application of transfer learning, few-shot learning and/or domain adaptation methods within the fields of AI and Data Science. Text Mining and/or Information Retrieval: we are also looking to expand our existing work in the area of Information Extraction and/or Information Retrieval, which lies at the interface between NLP and Data Science.
Natural Language Processing: we are looking for new colleagues to complement and extend our existing work in various areas of NLP and Computational Linguistics. One area of particular interest is the topic of our new AiNed project "Dealing with Meaning Variation in NLP". If you are excited to actively participate and collaborate in shaping our developing department, we look forward to receiving your application. In your application, please indicate which research area you are primarily interested in (1-6 above). In addition, you are also encouraged to indicate other potential research areas within the department to which your research could potentially be relevant.