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Research Assistant in Applied Statistical Modelling and Machine Learning

Technical University of Denmark - Section for Statistics and Data Analysis

Location: Lyngby - Denmark
Salary: Not Specified
Hours: Full Time
Contract Type: Permanent
Placed On: 16th August 2019
Closes: 15th September 2019

Starting at October 1st 2019, or as soon as possible thereafter, a postdoc position is offered at the Section for Statistics and Data Analysis, a part of Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU Compute). Our department DTU Compute is an internationally unique academic environment spanning the science disciplines mathematics, statistics and computer science. At the same time we are an engineering department, covering informatics and communication technologies (ICT) in their broadest sense. Finally, we play a major role in addressing the societal challenges of the digital society where ICT is a part of every industry, service, and human endeavor.

You will be working within the area of applied statistics, focusing on synthesizing statistical modeling with tools such as statistical learning; aspects of machine learning, deep learning and standard statistical analysis tools where appropriate, based on a data driven approach. Both method justification in terms of theoretical considerations and practical feasibility will be part of the work.

Responsibilities and tasks

PACE – Proactive Care for the Elderly with Dementia - is a partnership formed by DTU Compute, Center for Design, Innovation and Sustainable Transition at Aalborg University Copenhagen, Skovhuset (Hillerød Municipality), The SIF group, Copenhagen Business Hub and Kullegaard. PACE is financed by Innovation Fund Denmark.

You will, as part of PACE, explore Big Data to detect changes in practices among elderly with dementia, aiming at preventing hospitalizations, by combining information from already existing technologies.

  • With basis in longitudinal sensor information from intelligent floors, sensor staffs, alarm calls and additional sensor- and other information, you will build a procedure that, for each of the individual nursing home residents followed, will construct a concept of ‘normal behavior’, detected on an automatized basis. You will make the construction in such a way that natural variation in sensor readings etc. is incorporated to not indicate that the resident is changing behavior and will not trigger a violation of the individual ‘normal behavior’ concept.
  • You will detect and classify changes that fall outside normal behavior, essentially giving a pointer towards which disease (if any) that caused the deviation, and the severity of the implications.
  • You will be participating in the implementation of such a system.

Candidates should have a master’s degree in engineering or equivalent.

You should have a Masters degree or equivalent academic qualifications within mathematics/statistics, computational science and engineering, engineering, or equivalent areas. Programming skills in at least one language such as R, Matlab, Python, Java or C is essential.  

You should, in addition, have an interest in seeing mathematics, statistics and machine learning be put to practical use, and appreciate to operate among professionals from several disciplines far from technical science, while still being placed in a department that has this as its main focus.

  • You have the ability to deal with statistical modeling in Big Data, the central qualification.
  • You have excellent collaboration skills to match close collaboration and an interdisciplinary environment, and mastering of the English language, are essentials.
  • At the same time, you are innovative and enterprising, and enjoy sharing your ideas with colleagues.

Application procedure

To apply, please read the full job advertisement at

Application deadline: 15 September 2019

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* Salary has been converted at the prevailing rate on the date placed
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