PhD Candidate in Brain-Inspired HPC for Complex Optimization

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About the Role

SpiNNcloud Systems is offering a fully funded PhD position focused on developing next-generation High-Performance Computing approaches for large-scale optimization workloads.

The project investigates how brain-inspired, massively parallel architectures can serve as a new class of energy-efficient HPC solvers for complex industrial optimization problems. The research will be deployed and validated on SpiNNcloud’s production-grade supercomputing platform.

The candidate will be based at SpiNNcloud in Dresden and enrolled as a PhD student at TU Dresden within an EU-funded Doctoral Network.

Research Focus

Large-scale optimization problems are foundational to logistics, energy systems, transportation, and advanced industrial processes. At the same time, conventional HPC architectures increasingly face efficiency and scaling limitations for sparse and irregular workloads.

This PhD project explores:

  • Mapping large-scale optimization workloads to massively parallel architectures
  • Designing and benchmarking novel HPC solvers on energy-efficient hardware
  • Comparing performance, latency, and energy efficiency against classical CPU/GPU approaches
  • Applying the developed methods to real-world industrial use cases

The goal is to evaluate and advance a new computational paradigm for scalable, energy-efficient HPC

Environment

The candidate will join SpiNNcloud’s team of experts in algorithms, software systems, and high-performance hardware design.

The project is part of an EU Doctoral Network (MINDnet), including 15 PhD researchers across leading European universities and research institutions. The candidate will participate in international secondments and structured training programs.

Academic supervision will be conducted in collaboration with TU Dresden. For more information on the PhD programs and enrolment requirements at TU Dresden, please see Information about PhD studies.

Requirements

  • Master’s degree (120 ECTS or equivalent) in Electrical Engineering, Computer Science, Physics, Applied Mathematics, or a related field
  • Strong background in numerical methods, optimization, parallel computing, or HPC
  • Interest in scalable and energy-efficient computing architectures
  • Willingness to participate in international research exchanges

Due to program mobility rules, applicants must not have resided in Germany for more than 12 months within the last 36 months prior to recruitment.

If you are applying from abroad, you may find useful information on working in Germany on the website of the Federal Government and the International Students page of TU Dresden.

What We Offer

  • 3-year fully funded PhD position (TV-L E13 level)
  • Research at the intersection of HPC, large-scale optimization, and next-generation compute architectures
  • Integration into a fast-growing European deep-tech company commercializing supercomputing systems
  • Collaboration with leading academic and industrial partners

Start date: July 2026 (latest October 2026)
Location: On-site in Dresden

Application procedure

Your complete online application must be submitted no later than 15 March 2026 (23:59 German time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

  • A letter motivating the application (cover letter)
  • Curriculum vitae 
  • Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale

You may apply prior to ob­tai­ning your master's degree but cannot begin before having received it.

Applications received after the deadline will not be considered.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. In accord with the rules for the Marie Skłodowska-Curie program, the recruitment process will be open, transparent, impartial, equitable and merit-based while avoiding conflicts of interest. As SpiNNcloud System works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position.

The recruitment is taking place following the European Code of Conduct for Recruitment of Researchers, which all candidates are encouraged to study.

Funding note

This project has received funding from the European Union’s Horizon 2020 Research and innovation Program under the Marie Skłodowska-Curie Grant Agreement No. 101226674

References

[1] Gonzalez, Hector A., et al. "SpiNNaker2: A large-scale neuromorphic system for event-based and asynchronous machine learning." arXiv preprint arXiv:2401.04491 (2024).

[2] Chen, Z., Xiao, Z., Akl, M. et al. ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers. Nat Commun 16, 3086 (2025). https://doi.org/10.1038/s41467-025-58231-5

[3] Aimone, James B., et al. "A review of non-cognitive applications for neuromorphic computing." Neuromorphic Computing and Engineering 2.3 (2022): 032003.

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