丹麦科技大学

Modeling Large-scale Social Networks

项目介绍

DTU Compute’s Sections for Cognitive Systems would like to invite applications for a 3-year PhD position starting October 1st, 2021. The project is financed by the Villum Foundation Synergy Project “Nation-Scale Social Networks”, carried out jointly with the Center for Social Data Science (SODAS) at the University of Copenhagen. The PhD will be supervised by Professor Sune Lehmann and Professor Morten Mørum 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.

SODAS at University of Copenhagen
New types of data, in particular digital data, are flooding the social sciences. The broad catchphrase for the analysis of such data is ‘data science’. The Faculty of Social Sciences has made new, digital forms of data – sometimes collectively known as big data – and the integration of such data with social scientific modes of inquiry a priority at the Faculty. We call this integration Social Data Science, with research, carried out in an inter-departmental center comprising researchers from across the social sciences.

Project Description
The project is part of a larger project which investigates representations of social behavior and how predictive such representations are for life outcomes (education levels, income and wealth ranks, unemployment histories) based on registry data at Statistics Denmark. We are currently working on developing dense embeddings of life-event space, based on trajectories of life-events, using ideas from text embeddings. That work leverages recent literature on predicting disease outcomes based on patient records and explainability and interpretability are important considerations in our modeling.

This project will work on establishing network data to connect the uses. These networks will be based on data already contained in Statistics Denmark (family relations, joint workplaces, etc) or on external data from collaborators. Once the network dataset has been established, the work will focus on addressing the foundational question on the role of social networks for life outcomes. The aim is to address the role of the network in stages. Firstly, we will explore how well we can predict life outcomes from features based on registry data and network, explicitly comparing the relative magnitude of the contributions from network and structural features. Secondly, we will explore combining nodal and network features in a deep learning framework.

Responsibilities and tasks
The successful student will

  • Analyze large scale network data combined with rich nodal metadata.
  • Collaborate with researchers from both computational and social sciences in a truly interdisciplinary environment.
  • Co-author scientific papers aimed at high-impact journals
  • Participate in international conferences.
  • Participate advanced classes to improve their academic skillset
  • Carry out work in the area of dissemination and teaching as part of the overall PhD education.

Qualifications
You must 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. SODAS is located in the heart of Copenhagen. 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 October 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
Please submit your online application no later than 9 August 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

  • You must 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 2021-08-09
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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