丹麦技术大学动态复杂网络建模的机器学习博士后职位

丹麦技术大学动态复杂网络建模的机器学习博士后职位

POSTDOC IN MACHINE LEARNING FOR CONTINUOUS TIME MODELING OF DYNAMIC COMPLEX NETWORKS

DTU Compute would like to invite applications for a 20-month PostDoc position with starting date 1 February 2021 (starting time negotiable). The position is financed by DFF project “Learning the Structure and Dynamics of Complex Networks” and the PostDoc will be supervised by Professor Morten Mørup and Professor Sune Lehmann at the Section for Cognitive Systems, DTU Compute.

The project aims to develop novel computational frameworks for the analysis of dynamic social networks in continuous time circumventing typical network time- discretization by accounting for time-resolved interaction events. In particular, we are interested in scalable computational procedures for continuous time statistical dynamic network modeling using latent embeddings as well as efficient predictive procedures forecasting network dynamics. The developed continuous time dynamic network analysis tools will be used to analyze imminent challenges within our digital society enabling us to describe the dynamics of knowledge production, predict information propagation, and spot filter bubbles. The project comprises an interdisciplinary team of researchers (in addition to the DTU team, collaborators include Yong-YeolAhn at Indiana University and Martin Rosvall at Umeå University).

Qualifications Candidates should have a PhD degree in engineering science or natural science or equivalent academic qualifications. Furthermore,

You must have a relevant background within machine learning.

Experience in time-series analysis is an advantage.

Experience with modeling complex networks is an advantage.

Extensive experience programming in Python.

An active interest in strong collaborations and interdisciplinary work is a strong plus.

You must be fluent in English, both speaking and writing and possess excellent communication skills.