PhD Fellowship/Scholarship in Data Analytics and Machine Learning on Compressed Data

Emerging Scholars Council; Denmark; On-site; DKK 299,000 - 359,000; Full time

Job summary

Aarhus University, Graduate School of Technical Sciences, Denmark, invites applications for a PhD Fellowship/Scholarship within the Electrical and Computer Engineering programme. The position starts 1 October 2026 or later.

The CRISPER-IoT project targets edge processing to reduce storage, cost and cloud reliance, enabling greener, data‑driven infrastructures. Ideal candidates hold a relevant Master’s degree and have a strong research background in related areas.

Qualifications

• Applicants should hold a relevant Master’s degree (or be close to completing one) in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or a related discipline.
• Strong academic results at Bachelor’s and Master’s levels.
• Experience with scientific programming or software development is required.

Skills

Data analytics

Machine learning

Signal processing

Networked and cyber-physical systems

Software engineering

Education

Master's degree in Electrical Engineering / Computer Engineering / Computer Science / Applied Mathematics

Tools

Python

C/C++

Rust

Job description

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.

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.

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.