Postdoc / Research Assistant in Machine Learning

Tuesday 04 Feb 20

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The Section for Cognitive Systems at DTU Compute is looking for a post doc/research assistant within the area of machine learning and signal processing. The starting date is April 1st or as soon as possible thereafter. The position is part of the PORDEX project funded by the Danish Ministry of Defence - Acquisition and Logistics Organisation. The objective of the PORDEX project is to make the world a safer place, by developing an easy to use handheld detector, which can detect improvised explosives and precursors. The Danish company CRIM-Track Aps heads the project in close collaboration with DTU Chemistry, DTU Compute, the Danish Emergency Management Agency, and Cranfield University, Centre of Defence Chemistry (UK).

The Section for Cognitive Systems is a lively and research oriented group of scientists and support staff with a shared interest in information processing in man and in computers, and a particular focus on the signals they exchange - audio, imagery, and behaviour.

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 in their broadest sense. Finally, we play a major role in addressing the societal challenges of the digital society where IT is a part of every industry, service, and human endeavour.

DTU Compute strives to achieve research excellence in its basic science disciplines, to achieve technological leadership in research and innovation, and to address societal challenges in collaboration with partners at DTU and other academic institutions, nationally and internationally, and, equally important, with industry and organisations. We communicate and collaborate with leading centres and strategic partners in order to increase participation in major consortia.

DTU Compute plays a central role in education at all levels of the engineering programmes at DTU - both in terms of our scientific disciplines and our didactic innovation.

The project has the goal to complete the development of a portable Sniffer that can detect improvised explosives and precursors for improvised explosives. The Sniffer will add to the safety of the personnel whose job it is to find and disarm bombs made by improvised explosives and search the laboratories where the bombs are built. 

Responsibilities and tasks
You will be responsible for analysing the acquired data in the project and develop supervised machine learning methods for modelling the time responses from the chemical colorimetric sensor. You will also take part in creating sensor optimisation experimental designs together with the other partners in the project. We will also Nuclear Magnetic Resonance (NMR) spectroscopy to characterise the chemicals so possible research avenues can also go into machine learning for NMR.

As part of this team, you will be responsible for: 

  • developing machine learning models that performs time-series classification
  • identify important chemical compounds on the sensor using machine learning
  • testing and comparing different approaches in real-world settings and take part in designing the data collection process
  • identify individual baselines
  • estimating the reliability of the models and resulting classifications
  • lead the publication of academic papers in high-impact peer-reviewed journals or conferences
The role includes collaboration with other members of the team, both at DTU Compute, DTU Civil Engineering and our industrial- and research partners.

For the post doc position candidates should have a PhD degree or equivalent. For the research assistant position, 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, and additionally 1-3 years of work experience using machine learning in either industry or academia.

We are looking for candidates that preferably have a good track record working in one or more of the following research areas: 
  • machine learning and deep learning
  • time series analysis
  • signal processing
In addition, you:  
  • have strong programming skills in Python but can also work in MATLAB
  • have experience with implementing deep neural networks in either Pytorch or Tensorflow
  • have experience with experimental design and analysis
  • have a strong mathematical foundation
  • have strong analytical and academic skills
  • enjoy working with complex topics
  • have compelling communication skills in English, with a track record of scientific publications, conference presentations, reports and/or popular dissemination
  • have the ability and drive to work cross-disciplinarily in an international academic and industrial research setting
We offer
DTU is a leading technical university globally recognised 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 characterised by collegial respect and academic freedom tempered by responsibility.

Salary and terms of employment
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 period of employment is one year. 

The workplace is primarily at DTU Lyngby Campus but can also involve work at our research partner.

You can read more about career paths at DTU here

Further information
Further information may be obtained from Tommy Sonne Alstrøm, tel.: +45 4525 3431.  

You can read more about DTU Compute and COGSYS at

Application procedure
Please submit your online application no later than 19 February 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: 
  • Application (cover letter)
  • CV
  • Diploma (MSc/PhD)
  • List of publications
Applications and enclosures received after the deadline will not be considered.

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

DTU Compute has a total staff of 400 including 100 faculty members and 130 Ph.D. students. We offer introductory courses in mathematics, statistics, and computer science to all engineering programmes at DTU and specialised courses to the mathematics, computer science, and other programmes. We offer continuing education courses and scientific advice within our research disciplines, and provide a portfolio of innovation activities for students and employees. 

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.