We are looking for an outstanding and motivated postdoctoral researcher in the topics of graph analytics and network inference. The aim of the project is to investigate novel methdologies and develop efficient algorithms for mining large-scale graph data. Areas of interest include structure discovery in networks, active learning and exploratory graph analysis, and algorithms for labeled and temporal networks. Successful applicants are expected to have an excellent research track record in data mining, machine learning, and/or combinatorial optimization.
We are looking for a highly-qualified and motivated doctoral student on the topic of active learning and exploratory analysis in graphs. Successful applicants are expected to have completed successfully a Masters degree from a reputable international university, and have familiarity with graph mining, machine learning, and/or combinatorial optimization.
Apply to these open positions by sending an email to Aris Gionis, including a CV and statement of purpose.