Christian Mayr
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PhD Position in Efficient Language Models and AI Hardware Deployment at TU Dresden TU Dresden in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Funded research associate / PhD student position (E 13 TV-L) with full PhD opportunity; the post states financing is available and the role is limited until May 31, 2029, with a duration of about 30 months starting as soon as possible.
Deadline
Oct 20, 2026
Country
Germany
University
TU Dresden
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About this position
TU Dresden is advertising a Research Associate / PhD Student position at the Chair of Highly-Parallel VLSI Systems and Neuro-Microelectronics within the Faculty of Electrical and Computer Engineering.
The project is part of Horizon Europe OptimAIse and focuses on efficient language models, GenAI, and AI hardware deployment on SpiNNaker2, a massively parallel European hardware platform developed at TU Dresden and commercialized by SpiNNcloud. The research aims to optimize large language models for inference and deployment using an existing software stack and ML compilers such as MLIR, with additional exploration of approaches like Mixture-of-Experts and other sparse, communication-avoiding model designs.
The successful candidate will conduct scientific research on efficient language models and hardware-aware deployment, develop and train sparse GenAI models for SpiNNaker2, implement model layers on hardware, and publish results in top-tier conferences and journals. The work also contributes to requirements and recommendations for next-generation AI hardware such as SpiNNaker3.
Eligibility: applicants should hold a Master’s degree or equivalent in computer science, electrical engineering, machine learning, or a related field. Strong programming skills in C++ and Python, good English, and the ability to work independently and in teams are required. Experience with LLVM/MLIR, embedded software, accelerator architectures, and parallel/distributed computing is an advantage.
Funding: the position is funded (E 13 TV-L) and offers the chance to obtain a PhD. The role starts as soon as possible and is limited until May 31, 2029.
Application deadline: October 20, 2026. Apply with a cover letter, CV, and degree certificates, preferably via the TUD SecureMail Portal, quoting reference code HPSN_OptimAIse_2026.
Funding details
Funded research associate / PhD student position (E 13 TV-L) with full PhD opportunity; the post states financing is available and the role is limited until May 31, 2029, with a duration of about 30 months starting as soon as possible.
What's required
Master’s degree or equivalent in computer science, electrical engineering, machine learning, or a related field; good understanding of LLMs and how they are processed on AI hardware for inference; very good programming skills in C++ and Python; very good written and spoken English; high motivation and ability to work independently and in teams. Beneficial experience includes compiler frameworks such as LLVM/MLIR, embedded software development, computer and accelerator architectures, and parallel/distributed computing.
How to apply
Prepare a detailed application with cover letter, CV, and degree certificates. Submit it preferably via the TUD SecureMail Portal as a single PDF and quote reference code HPSN_OptimAIse_2026. Applications can also be sent by email or post to Prof. Christian Mayr at TU Dresden.
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