DTU PhD Positions – Jobs for Computer Science Engineering Degree, Machine Learning Experience

    DTU PhD Positions - Jobs for Computer Science Engineering Degree, Machine Learning Experience

    PhD Position for Graduates in Computer science, Engineering science, Natural science with a Machine learning experience to work in the project “DFF project – Learning the Structure and Dynamics of Complex Networks” at Section for Cognitive Systems, DTU compute, Technical University of Denmark, Denmark (Jobs for Computer Science Engineering Degree)


    Deadline to Apply

    May 23, 2021 (23: 59 GMT +1)


    Overview

    PositionPhD Position
    No. of Position(s)One
    Research Area– Computer science
    – Engineering science
    – Natural science
    ScholarshipAccording to standard norms
    WorkplaceSection for Cognitive Systems
    DTU Compute
    Technical University of Denmark,
    Denmark
    Contract Period3 Years

    Qualifications

    • A master’s degree in engineering science or natural science or equivalent academic qualifications.
    • Strong background within machine learning and programming in Python as well as analysis and modeling of complex networks.
    • Fluent in English, both speaking and writing, and possess excellent communication skills.

    Preference

    • Experience writing and publishing scientific papers is an advantage.

    Responsibilities

    • 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.

    About the Project

    • The project is financed by the DFF project “Learning the Structure and Dynamics of Complex Networks”.
    • 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.

    About Section for Cognitive Systems

    Modern life is embedded in intelligent systems, from the mind reading Internet to hearing aids listening in on our conversations. Advanced data analysis is increasingly a determinant for productivity and personal quality of life.

    The Section for Cognitive Systems research 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.

    How to Apply?

    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.

    Documents Required

    Applications must be submitted as a single PDF file containing all materials to be given consideration. 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)

    Note

    • If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.
    • The assessment of the applicants will be made by Professor Sune Lehmann, DTU Compute and Professor Morten Mørup, DTU Compute.
    • All interested candidates irrespective of age, gender, race, disability, religion, or ethnic background are encouraged to apply.

    Inquiries

    Professor Sune Lehmann
    E-mail: sljo@dtu.dk

    Official advertisement

    PhD project in Characterizing Temporal Social Networks using Dynamic Embeddings

    #Jobs for Computer Science Engineering Degree candidates

    Post expires at 8:59am on Monday May 24th, 2021 (GMT+9)