Have you recently finished your PhD? And would you like to come to Denmark to work with deep learning on an amazing dataset? Then keep reading. There’s a great opportunity for DTU funding that we can apply for together
Proposal: Deep learning, network structure, and language on Twitter
Based on a massive dataset (10% of all tweets going back to 2012), we wish to study the interplay between language and network structure. Specifically, we wish to study the interplay between language evolution and network evolution across time (effectively the co-evolution of language and network structure).
As part of the grant application, you will be part of shape the research questions, but a rough idea would be to use deep learning approaches (word embeddings, LSTMs) to represent the language component, and state-of-the-art network science approaches for the network evolution.
Advisors and further information
- Associate Professor, Sune Lehmann, DTU Compute. https://sunelehmann.com/
- Professor, Lars Kai Hansen, DTU Compute. http://cogsys.imm.dtu.dk/staff/lkhansen/lkhansen.html/
- Associate Professor, Alan Mislove, Northeastern University. http://www.ccs.neu.edu/home/amislove/
Main practical requirements for the COFUND grant
- At the time of recruitment (1 July 2017) applicants must not have resided or carried out their main activity in Denmark or at DTU for more than 12 months in the 3 years immediately prior to recruitment (excl. holidays and short visits)
- Successful applicants must move to Denmark by the time of employment at the latest;
- The applicant must, by the time of recruitment (1 July 2017), be in possession of a doctoral degree or have at least 4 years of full-time equivalent research experience
Detailed info regarding DTU COFUND: http://www.dtu.dk/english/Research/Research-at-DTU/HC-Oersted-Postdoc-COFUND
- Contact Sune at firstname.lastname@example.org for further information. Note that the grant application to COFUND must be a collaboration between faculty at DTU and an interested applicant.
Candidates must have a strong publication record within deep learning and/or network science.