丹麦科技大学

Characterizing Temporal Social Networks using Dynamic Embeddings

项目介绍

DTU Compute’s Sections for Cognitive Systems would like to invite applications for a 3-year PhD position starting July 1st, 2021. The project is financed by theDFF project “Learning the Structure and Dynamics of Complex Networks”. The PhD will be supervised by Professor Sune Lehmann (main supervisor) and Professor Morten Mørup at the Section for Cognitive Systems, DTU Compute.

The Cognitive Systems Section at DTU:
Advanced data analysis is increasingly a determinant for productivity and personal quality of life. The Section for Cognitive Systems researches information processing in man and computer, with a particular focus on the signals they exchange – audio, imagery, behavior – and the opportunities these signals offer for modeling and prediction. Our research is based on statistical machine learning and signal processing, on quantitative analysis of digital media and text, on mobility and complex networks, and on cognitive psychology.

Project Description
The PhD project is part of a larger project which investigates how to develop computational tools for the analyses of large dynamic complex networks that can i) enable a human understanding of the structure of these complex systems and ii) forecast their future behaviors? The project comprises an interdisciplinary team of researchers focusing on both machine learning and social network analysis. Other project researchers are currently working on developing novel computational frameworks and scalable computational procedures for statistical dynamic network modeling using latent embeddings and efficient predictive procedures forecasting network dynamics.

The aim of this PhD is to work with these dynamic network analysis tools to analyze and understand phenomena within our digital society, through better prediction and anomaly detection. For example to model and visualize the large-scale dynamics of knowledge production, to forecast information propagation, and reveal filter bubbles.

Responsibilities and qualifications
The successful student

  • Is an experienced programmer, e.g. in Python.
  • Is unafraid to use APIs, scrape data, and has worked with “unstructured” data from the web.
  • Has experience working with network data, e.g. via NetworkX, graph-tool or similar.
  • Is used to working with machine learning methods.
  • Is interested in (and has experience with) data visualization.
  • Will work on analyses of large scale dynamic network data.
  • Will co-author scientific papers aimed at high-impact journals, participate in international conferences, and participate in advanced classes to improve their academic skillset
  • Is motivated to collaborate with researchers from both computational and social sciences in a truly interdisciplinary environment.
  • Is motivated to disseminate their research though popular talks and on social media, and to teach as part of the overall PhD education.

You should have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree. You must have a master’s degree in engineering science or natural science or equivalent academic qualifications. You must have a very strong background within machine learning and programming in Python as well as analysis and modeling of complex networks. Experience writing and publishing scientific papers is an advantage. You must be fluent in English, both speaking and writing, and possess excellent communication skills.

Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in the DTU Compute PhD School Programme. For information about the general requirements for enrolment and the general planning of the PhD study program, please see the DTU PhD Guide.

Assessment
The assessment of the applicants will be made by Professor Sune Lehmann, DTU Compute and Professor Morten Mørup, DTU Compute.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation, and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

The position is in the Section for Cognitive Systems at the Technical University of Denmark, which is a top Danish machine learning group. Both salary and working conditions are excellent. The group is a down-to-earth and fun place to be. Most group members live in Copenhagen which is often named as the best city in the world to live in, and for good reasons. It’s world-renowned for food, beer, art, music, architecture, the Scandinavian “hygge”, and much more. In Denmark, parental leave is generous, and child-care is excellent and cheap.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The position is a full-time position. The period of employment is 3 years starting July 1st, 2021 (or as soon as possible thereafter).

You can read more about career paths at DTU here.

Further Information
Further information concerning the project can be obtained from Professor Sune Lehmann, sljo@dtu.dk.

Further information concerning the application is available at the DTU Compute PhD homepage.

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.

Application
Your complete online application must be submitted no later than 23 May 2021 (Danish time). Applications must be submitted as a single PDF file containing all materials to be given consideration. To apply, please open the link “Apply online”, fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

  • A letter motivating the application (cover letter)
  • Curriculum vitae
  • Grade transcripts and BSc/MSc diploma
  • Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here)

You may apply prior to obtaining your master’s degree but cannot begin before having received it.

All interested candidates irrespective of age, gender, race, disability, religion, or ethnic background are encouraged to apply.

录取要求

  • Is an experienced programmer, e.g. in Python.
  • Is unafraid to use APIs, scrape data, and has worked with “unstructured” data from the web.
  • Has experience working with network data, e.g. via NetworkX, graph-tool or similar.
  • Is used to working with machine learning methods.
  • Is interested in (and has experience with) data visualization.
  • Will work on analyses of large scale dynamic network data.
  • Will co-author scientific papers aimed at high-impact journals, participate in international conferences, and participate in advanced classes to improve their academic skillset
  • Is motivated to collaborate with researchers from both computational and social sciences in a truly interdisciplinary environment.
  • Is motivated to disseminate their research though popular talks and on social media, and to teach as part of the overall PhD education.
  • You should have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree. You must have a master’s degree in engineering science or natural science or equivalent academic qualifications.
  • You must have a very strong background within machine learning and programming in Python as well as analysis and modeling of complex networks.
  • You must be fluent in English, both speaking and writing, and possess excellent communication skills.

申请亮点

  • Experience writing and publishing scientific papers is an advantage.

项目概览

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北欧, 丹麦 所在地点
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截止日期 2021-05-23
丹麦科技大学

院校简介

丹麦技术大学坐落于北欧丹麦王国-哥本哈根大区,由著名物理学家奥斯特于1829年创建。
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联系方式

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