Data Analytics and Machine Learning on Compressed Data

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Job Details

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position is available from 1 October 2026 or later. You can submit your application via the link under 'how to apply'.

Research Area And Project Description The rapid expansion of IoT devices produces vast data, not all of which is relevant. In fact, only a fraction is used in real time. Current practice sends nearly all data to the cloud for processing and storage, incurring high costs and bandwidth use despite varying data importance. At the same time, organisations face growing demands for sustainability, security, and efficiency amid climate and societal pressures. Processing closer to sensors, i.e. at the edge, improves reaction time, optimises resources, and helps tag data for further analysis. However, hardware constraints in IoT systems (e.g. memory, storage, processing speed) often prevent advanced AI/ML or data-heavy workloads at the edge, keeping centralised processing dominant.

Reducing storage, communication, and processing costs is critical to accelerating digitalisation of key infrastructures, incl. healthcare, transport, water, energy, while lowering reliance on cloud infrastructure and electricity use. Cost reductions benefit advanced economies by enabling more SMEs to digitalise and foster global inclusion by lowering operational and infrastructure barriers. In all cases, retaining control of data and analysis is essential to digital sovereignty.

The CRISPER-IoT project will accelerate Denmark’s green transition by digitalizing critical infrastructure to cut operational costs and increases global competitiveness by reducing the reliance on expensive cloud resources and boosting Edge processing. Aarhus Univ. (AU), FORCE Technology (FT), Onics (ON), Iterator IT (IIT), Aarhus Vand (AV), SenArch (SA), KI Monitoring (KI), and the Kigali Collaborative Research Center (KCRC) unite to achieve this goal.

CRISPER-IoT delivers an end-to-end solution that compresses data at its source and keeps it compressed throughout its lifecycle. Pioneered by AU, these advanced compression techniques allow for on-the-fly data compression, support analytics and machine learning (ML) without decompression, significantly reduce algorithm complexity and memory use, and enable much of the analysis and decision-making to the Edge.

We are seeking a highly motivated PhD student to work on these topics with our internal team and in close collaboration with our industrial partners. Particularly, we are interested in students with a passion for data analytics, machine learning, data communications, and/or data compression and with willingness to learn and develop new ideas with direct impact to industry.

Qualifications And Specific Competences Applicants should hold a relevant Master’s degree (or be close to completing one), including, one in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or a related discipline.

A Successful Candidate Should Have

• A strong academic background with good results at both Bachelor’s and Master’s levels.
• Solid foundations in one or more of the following areas:
  • Data analytics and machine learning
  • Data Communications, information theory and/or data compression
  • Signal processing
  • Networked and cyber-physical systems
  • Software engineering
• Good analytical and mathematical skills.
• Experience with scientific programming or software engineering (e.g., Python, C/C++, Rust, or similar).
• Experience in industry projects is a plus.
• Excellent written and oral communication skills in English.
• Experience with experimental platforms, embedded systems, or wireless testbeds is a plus.

The candidate is expected to have a strong motivation for pursuing a research career and to contribute actively to the scientific environment of the Communications, Control and Automation section through research, collaboration, teaching-related activities, and dissemination of research results.

Place of Employment and Place of Work The place of employment is Aarhus University, and the place of work is Department of Electrical and Computer Engineering, Helsingforsgade 10, 8200 Aarhus N., Denmark.

Contacts Applicants seeking further information regarding the PhD position are invited to contact:

• Professor Daniel Enrique Lucani Rötter, e-mail: daniel.lucani@ece.au.dk (main supervisor)

For information about application requirements and mandatory attachments, please see our application guide. If answers cannot be found there, please contact:

• admission.gradschool.tech@au.dk

How To Apply Please follow this link to submit your application.

Application deadline is 1 August 2026 23:59 CEST.

Preferred Starting Date Is 1 October 2026. For technical reasons, you must upload a project description. Please simply copy the project description above and upload it as a PDF in the application.

Please Note

• Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be taken into account.
• The programme committee may request further information or invite the applicant to attend an interview.
• Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.

Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.