Jobs & theses

Development of tools and instruments for measuring and analyzing the performance of edge applications in IT systems (e.g. servers, user devices). Develop new IT system-related approaches to optimize the performance of edge applications. Collaboration in a state-of-the-art laboratory environment, with a multinational and globally networked team of IT, network and application specialists.

Development of tools for measuring and analyzing network performance with edge applications. Development of new network-related approaches to optimize edge application performance. The opportunity to work in a world-class lab environment that includes a 5G network and a high-performance server cluster. A multinational team of experts from different fields, such as IT, network and application development.

Leading a small team of software engineers and developers. Managing the software development process from requirements analysis and software architecture to implementation and testing. Working in a modern lab environment (5G campus network, high-performance server cluster). Working in an interdisciplinary, international team of IT, network and application experts.

The dissertation addresses the challenge of efficiently managing access to computing resources in edge environments where requirements can change rapidly. The focus is on reinforcement learning (RL), an intelligent technique that enables automatic fine-tuning of control systems, making them more efficient in complex and changing situations.

Required skills:

Mastery of RL principles, good programming skills in Python or similar languages, and experience with machine learning frameworks.

Evaluate the role of PID controllers in managing VM and container admissions under various conditions and loads, ensuring consistent application performance and optimal utilization of server resources. Students will integrate a deployed controller into the server setup.

Required Skills:

Knowledge of control theory and good programming skills in Python and Bash scripting are essential to carry out this work.

The challenge:

The democratization of edge computing through Open Edge Access (OEA) leads to challenges in resource allocation. Traditional resource allocation is reaching its limits in addressing the openness, variability and collaboration aspects of OEA.

Aim of the research:

The research aims to develop efficient strategies for resource allocation in OEA environments, overcoming challenges such as limited knowledge of resource availability and dynamic changes in resource availability.

Main research activities:

• Evaluate existing algorithms for open edge access environments.
• Investigate and identify key challenges and limitations of traditional resource allocation methods in OEA scenarios.
• Development of techniques to proactively identify available resources in decentralized networks.
• Develop algorithms capable of predicting future resource requirements and adjusting allocation decisions in real-time

Required skills:

Proficiency in Python and Golang

The challenge:

Modern peer-to-peer (P2P) networks are dynamic, with dynamic nodes and fluctuating performance metrics. How can we efficiently manage and record essential network state information?

Research Objective:

To create a resilient, adaptive Distributed Ledger Technology (DLT) in a P2P network of edge servers. This system will cope with the dynamic changes in the network and ensure reliable data management and performance optimization.

Main research activities:

• Automating the collection of key network metrics such as latency, bandwidth and node capacity in real time.
• Integration of blockchain technology for secure and transparent recording of network status
• Development of algorithms for edge servers to automatically select the best network based on performance metrics and historical data.

Required skills:

Proficiency in Python, Golang, JavaScript and Solidity.

Unleash your research potential! Join us in our visionary master thesis project and shape the future with innovative Industry 4.2 solutions combining Edge Impulse and Raspberry Pi for real-time data analytics, predictive maintenance and scalable condition monitoring.

Required skills:

Proficiency in programming languages such as Python, Linux and Javascript is required.