PhD scholarship in Extracting Essential Data and Making Inference from Big Data

Wednesday 06 May 20

Apply for this job

Apply no later than 1 June 2020
Apply for the job at DTU Compute by completing the following form.

Apply online

DTU Compute would like to invite applications for a 3-year PhD position starting September 1, 2020. The position is financed by DTU and is part of a collaboration together with the University of Bergen, Norway. The University of Bergen has announced a PhD position in parallel. The two candidates are expected to collaborate on the common topic and they are required to have both short and long external visits at the partner institution.

The PhD will be hosted in the Statistics and Data Analysis Section at DTU Compute. Our Section has strong research in industrial statistics, big data and machine learning techniques and a good collaborative environment.

Big data is often accompanied with undesired noise, missing values, and imbalanced data. Furthermore, from this large data only a small fraction is relevant for creating the value

of interest.

This project seeks to find a smaller data representation (or even small data) which is equivalent to the original data in the following sense. Studying the smaller data is as relevant for the problem as the original one, subject to an acceptable loss of accuracy.

Through the collaboration with the University of Bergen and a combination of techniques from the world of statistical inference and machine learning with algorithmic and complexity tools, we wish to make big data projects easier to execute.

This project itself will be on statistical inference as a means to address the problem of data compression but with a twist that we will also address the algorithmic issues arising in the methodology. A range of machine learning methods could be relevant, but in this project, we will begin the focus with popular methods like auto encoders, regularizations, and sampling techniques. These will be just a start, and we hope to make new tools and techniques to study data compression in both theory and practice.

In essence, we will research how the methods for statistical inference are related to data compression methods when the objective is the same – get a small data that is as relevant as the original one. We will also look at synergies between these. 

Responsibilities and tasks
You will be responsible for undertaking research as described above. In order to encourage collaboration between you and your co-PhD student at UoB as well as the two departments, we aim to ensure:
 
  • You will have a main supervisor (and also a co-supervisor) at DTU Compute and most importantly also a co-supervisor at the University of Bergen.
  • Sometime in year 2 or year 3 of the studies a long research stay (3-6 months) is planned for you or your co-student at the partner university closely followed by a similar long research stay (3-6 months) by the other student at the opposite university.
  • Furthermore, during the studies you will have multiple short stays at University of Bergen.
Further responsibilities include: 
  • As a PhD candidate, you will have the opportunity and responsibility to teach BSc/MSc students in our regularly offered courses in data analytics.
  • You will also have the opportunities to develop your teaching skills in our distance learning and continuing education programs through short courses offered primarily for the industry.
  • Your research include several disciplines and it will therefore be important to seek information in and with different sources and master collaboration with other disciplines.
  • You must work independently, and at the same time collaboratively, and as such it will also be your responsibility to enhance the collaborations in the project.
Qualifications
Candidates 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 in data analysis, mathematical modelling, and computing and if you have practical experience with large datasets and machine learning methods, we highly encourage you to apply. Furthermore, prior knowledge in software packages such as R and Python as well as proficient coding skills will be required. You must be fluent in English, both speaking and writing with great communication and presentation skills.

Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide

Assessment
The assessment of the applicants will be made by Associate Professor Line Clemmensen and Professor Bjarne Ersbøll (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. 

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 full-time and the period of employment is 3 years beginning 1st September 2020 or according to mutual agreement.

You can read more about
career paths at DTU here

Further information
Further information may be obtained from Associate Professor Line Clemmensen, tel.: +45 4525 3764, email: lkhc@dtu.dk

You can read more about DTU compute at
www.compute.dtu.dk/english

Application
Please submit your online application no later than 1 June 2020 (23:59 local time). Applications must be submitted as one 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 (incl. list of publications or other research experience)
  • Grade transcripts and BSc and MSc diplomas
  • Excel sheet with translation of grades to the Danish grading system and course names translated into English (see guidelines and Excel spreadsheet here)
  • An example technical text you have written in English such as a report prepared for a course, a consulting project, or a published peer-reviewed scientific article.
If one or more of the items requested above is missing, the application will be considered invalid.

Candidates may apply prior to obtaining their master's degree but cannot begin before having received it.
 

Applications and enclosures received after the deadline will not be considered.

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

DTU Compute conducts research and provides teaching in the fields of mathematics, modeling and computer science. The expanding mass of information and the increasingly complex use of advanced technology in society demand development of advanced computer based mathematical models and calculations. The unique skills of the department are in high demand in IT innovation and production.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear vision to develop and create value using science and engineering to benefit society. That vision lives on today. DTU has 11,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. Our main campus is in Kgs. Lyngby north of Copenhagen and we have campuses in Roskilde and Ballerup and in Sisimiut in Greenland.