Job Openings

»PhD Student / Research Assistant (f/m/d): Secure and Efficient Computing in Emerging Systems «

For many years, the Chair for Embedded Systems (CES) has been internationally recognized for its research in embedded systems, resource-aware computing, and security. Many open research challenges in these areas must be addressed to ensure trustworthy, secure, and efficient computing in modern distributed and embedded environments.With the increasing complexity of connected and decentralized computing systems, ensuring efficiency, security, and transparency has become a key challenge. This is particularly relevant in embedded and resource-constrained environments such as edge
computing. Emerging technologies, including blockchain for secure computing, explainable AI (XAI) for system transparency, and machine learning for resource management, offer promising solutions but require further exploration to be effectively deployed in real-world systems. These challenges are especially relevant in the context of next-generation communication networks such as 6G, which demand low-latency, secure, and resource-efficient computing solutions.

We seek a highly motivated researcher (f/m/d) to contribute to our ongoing efforts in secure, explainable, and efficient computing through research in one or more of the following areas:
     ●  Embedded systems and resource-aware computing – developing techniques for efficient execution, adaptive resource management, and performance optimization.
     ●  Security and trust in computing systems – designing methods for data integrity, secure execution, and verification in distributed and embedded architectures.
     ●  Blockchain for trusted computing – investigating lightweight, decentralized security mechanisms for data protection, model verification, and tamper resistance.
     ●  Explainability and transparency in computing – developing techniques for interpretable and auditable system behavior in machine learning-driven and automated decision-making systems.
     ●  Machine learning for systems optimization – leveraging ML to enhance energy efficiency, real-time performance, and dynamic workload adaptation.
     ●  Efficient and secure computing in 6G networks – exploring low-latency and secure computing frameworks that integrate blockchain, explainability, and resource-aware AI techniques for future networked systems.

The selected candidate will work in a collaborative and interdisciplinary research environment, contributing to both fundamental research and practical system implementations.

Personal qualification:

The ideal candidate (f/m/d) must have a very good Master’s degree (or equivalent) in Computer Science (CS), Electrical Engineering (EE), or a related field, with a specialization in one or more of the following areas:
   ●    Embedded Systems & Resource Management (e.g., real-time computing, energy-efficient architectures, hardware/software co-design).
   ●    Security & Trusted Computing (e.g., blockchain, cryptographic protocols, secure system architectures).
   ●    Machine Learning for Systems (e.g., ML-driven optimization, AI for embedded applications, decision support systems).
   ●    Explainability & Transparency in Computing (e.g., interpretable AI, system auditability, explainable security mechanisms).
   ●    Decentralized and Trusted Computing (e.g., lightweight blockchain security, verifiable computing, distributed trust frameworks).

Additional skills that will be beneficial (but not mandatory) include:
   ●    Experience in Linux environments, programming in C/C++, Python, or knowledge of hardware/software security frameworks.
   ●    Familiarity with system simulators or or cryptographic security techniques.
   ●    Interest in privacy-preserving computing, lightweight consensus mechanisms, or AI explainability methods.

Fluency in written and spoken English is required. The ideal candidate should be highly motivated, capable of working independently, and interested in interdisciplinary research. Strong communication skills are essential for effective collaboration within the research team.

Please send your application, including:
   ●   A cover letter outlining your motivation and research experience.
   ●   Your CV.
   ●   Copies of certificates, transcripts, and references (if available).

We are committed to equal opportunities and encourage female researchers to apply.
Recognized severely disabled persons will be given preference if equally qualified.
 

»New paradigms for machine learning on non-volatile memory architectures«

Since many years, the Chair for Embedded Systems works internationally successful in the areas of computer engineering, such as smart embedded systems. Many interesting and open problems in these areas need to be addressed to successfully deploy such systems in modern application domains.

Embedded systems usually have limited energy, computing power, and memory/storage space. Hardware accelerators and revolutionary memory hierarchies are promising candidates to tackle these challenges. On the memory side, the emerging byte-addressable non-volatile memories (referred to as NVMs for short), such as Phase Change Memory, Spin-Transfer Torque RAM and Resistive RAM (ReRAM), feature low leakage power, high density, and low unit costs. NVMs are hence interesting alternatives to replace DRAM as main memory and/or hard disks and flash as storage. NVMs offer the opportunity for new memory architectures and open new computing paradigms like near-memory and memory computing.

In the scope of this research project, we want to investigate energy efficient machine learning applications on resource constraint devices/systems in which the underlying architectures profit from in-memory and/or near-memory computing.

Personal qualification:

• The candidate (f/m/d) must have a very good Master's degree or PhD degree (or equivalent) in CS or EE with background or specialization in some of following topics, i.e., NVM, machine learning for resource constraints systems, in-memory computing, near-memory computing.
• The ideal candidate (f/m/d) shows a strong interest and motivation to deepen in these topics to a level required for a doctorate or as a PostDoc.
• Experience and/or interest in C/C++, VHDL/Verilog, synthesis tools, and/or CPU/system simulators will be beneficial.
• Fluency in written and spoken English is a prerequisite.
• We are looking for a highly motivated candidate (f/m/d) with a strong commitment to research ethics and teamwork.
• Good communicative skills are mandatory due to the interdisciplinary structure of the project and the team.

• Low Power Design: Bei steigender Anzahl von Transistoren pro Chip und kaum weiter absinkender Versorgungsspannung ist die Wärmeentwicklung und der Stromverbrauch des gesamten Chips ein drängenderes Thema als noch vor einigen Jahren.
• Adaptive Mikro-Architekturen: Architekturen, die sich zur Laufzeit an die Anforderungen der Anwendungen anpassen können, erlauben es, die vorhandenen Transistoren effizienter zu nutzen. Die Verwaltung der Adaptivität und die Auswirkungen auf z.B. Sicherheitsaspekte oder Laufzeitgarantien sind die Herausforderungen in modernen Systemen.
• Zuverlässigkeit spielt eine immer größere Rolle auch vor dem Hintergrund von Variabilität und Schaltkreis-Alterung.
• Hardware Security
• Maschine Learning für Multi-Cores kann dabei helfen einen hohen Grad von Effizienz in einem hochdynamischen Multi-Core System zu erreichen.
• Dezentrale Ressourcenverwaltung ist vorteilhaft für einen hohen Grad an Skalierbarkeit in komplexen Multi-Core Systemen.