Research Associate / PhD Student /PostDoc (m/f/x) in Near-Memory Computing for RISC-V

HiPEAC; Dresden; Vor Ort; EUR 52.000 - 72.000; Vollzeit

Zusammenfassung

The Chair of Adaptive Dynamic Systems at TUD Dresden University of Technology invites applications for a full-time position as Research Associate / PhD Student / PostDoc (m/f/x) in Near-Memory Computing for RISC‑V. The role focuses on modeling, simulation, and ML-driven kernel placement, with international collaboration and publication opportunities.

The successful candidate will work on SystemC-based simulations, FPGAs, and architecture exploration, aiming for a high-impact academic

Qualifikationen

• M.Sc. or equivalent in computer engineering, computer science, electrical engineering or related field.
• Excellent C/C++ programming skills.
• Fluency in English; German is an advantage.
• Ability to work effectively in an international team.
• Strong background in computer architecture, RISC‑V, FPGA‑based systems, SystemC, VHDL/Verilog, HLS, AI/ML.

Aufgaben

• Modeling and simulation of near-memory computing architectures for RISC‑V processors using SystemC and FPGAs.
• Develop machine learning methods to identify suitable computational kernels and optimize their placement within the processor memory hierarchy.
• Contributing, administrating and reporting in (inter-)national research and development projects.
• Publishing and presenting results at international conferences.
• Close cooperation with academic and industrial cooperation partners.

Kenntnisse

C/C++ programming

English fluency

German knowledge

international teamwork

computer architecture

RISC-V

SystemC

FPGA-based systems

Ausbildung

M.Sc. or equivalent in computer engineering, computer science, electrical engineering or related field

Jobbeschreibung

TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally oriented, regionally anchored top university as it focuses on the grand challenges of the 21st century. It develops innovative solutions for the world’s most pressing issues. In research and academic programs, the university unites the natural and engineering sciences with the humanities, social sciences and medicine. This wide range of disciplines is a special feature, facilitating interdisciplinarity and transfer of science to society. As a modern employer, it offers attractive working conditions to all employees in teaching, research, technology and administration. The goal is to promote and develop their individual abilities while empowering everyone to reach their full potential. TUD embodies a university culture that is characterized by cosmopolitanism, mutual appreciation, thriving innovation and active participation. For TUD diversity is an essential feature and a quality criterion of an excellent university. Accordingly, we welcome all applicants who would like to commit themselves, their achievements and productivity to the success of the whole institution.

At the Faculty of Computer Science, Institute of Computer Engineering, the Chair of Adaptive Dynamic Systems offers a full-time position as

Research Associate / PhD Student /PostDoc (m/f/x) in Near-Memory Computing for RISC-V

(subject to personal qualification, employees are remunerated according to salary group E 13 TV-L)

starting as soon as possible. The position is limited to 2 years, with the option of extension. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz –WissZeitVG). The position aims at obtaining further academic qualification.

The Chair of Adaptive Dynamic Systems conducts research in the fields of reconfigurable computing, domain-specific computer architectures, networks-on-chip (NoCs), methods and algorithms for application parallelization, simulators and virtual platforms for application- and architecture exploration, hardware/software co-design and operating/runtime systems. Typical application domains are e.g. signal-/image processing and machine learning.

Tasks:

• modeling and simulation of near-memory computing architectures for RISC‑V processors using SystemC and field‑programmable gate arrays (FPGAs)
• developing machine learning methods to identify suitable computational kernels and optimize their placement within the processor memory hierarchy
• contributing, administrating and reporting in (inter-)national research and development projects
• publishing and presenting results at international conferences
• close cooperation with academic and industrial cooperation partners

Requirements:

• excellent university degree (M.Sc. or equivalent) in computer engineering, computer science, electrical engineering or a related field
• very good programming skills in C/C++
• fluency in English; knowledge of German is an advantage
• high self‑motivation, commitment, and flexibility, as well as the ability to work effectively in an international team
• strong background in one or more of the following areas: computer architecture, RISC‑V, FPGA‑based systems, SystemC, VHDL or Verilog, high‑level synthesis, compiler frameworks, artificial intelligence, and/or machine learning

We offer an excellent working environment in an international team with many career development possibilities.

TUD strives to employ more women in academia and research. We therefore expressly encourage women to apply. The university is a family‑friendly university. We welcome applications from candidates with disabilities. If multiple candidates prove to be equally qualified, those with disabilities or with equivalent status pursuant to the German Social Code IX (SGB IX) will receive priority for employment.

Reference to data protection: Your data protection rights, the purpose for which your data will be processed, as well as further information about data protection is available to you on the website: https://tu-dresden.de/karriere/datenschutzhinweis

Metadata

Topics: Accelerators, Approximate computing, Artificial intelligence, Compilation, Computer architecture, CPUs, Deep learning, Design Space Exploration, Edge computing, Energy efficiency / Low‑power computing, FPGAs, High-performance computing, Machine learning, Memory, Multicore / Manycore, Neural networks, Parallel computing, RISC‑V, Simulation

The Technische Universität Dresden is Germany's largest technical university, offering 129 disciplines across 14 faculties. It excels in Biomedicine, IT, and materials science, recognized as a University of Excellence since …

The HiPEAC project has received funding from the European Union's Horizon Europe research and innovation funding programme under grant agreement number 101296676. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.