PhD Candidate for Neuromorphic Approaches to Solving Combinatorial Optimization Problems Using [...]

SpiNNcloud Systems; Dresden; Vor Ort; EUR 40.000 - 50.000; Vollzeit

Zusammenfassung

A research-driven technology company in Dresden is seeking a PhD candidate to research combinatorial optimization using spiking neural networks. The role will involve participation in international collaborations as part of an EU-funded Doctoral Network. Candidates must have a master’s degree and a strong background in optimization and neuromorphic computing. This PhD opportunity offers unique experiences across multiple European institutions and competitive salary aligned with the German public-sector pay scale.

Leistungen

Competitive salary

Dynamic international environment

Collaborative research opportunities

Qualifikationen

• Must have a two-year master's degree or equivalent.
• Experience or familiarity with combinatorial optimization.
• Research skills in neuromorphic computing.

Aufgaben

• Explore spiking neural networks for solving optimization problems.
• Implement optimization algorithms on neuromorphic hardware.
• Participate in international team activities and outreach.

Kenntnisse

Combinatorial optimization

Neuromorphic computing

Programming skills

Research experience

Ausbildung

Master's degree (120 ECTS)

Jobbeschreibung

Overview

SpiNNcloud Systems is seeking a PhD candidate to research solving combinatorial optimization problems using spiking neural networks and efficient hardware deployment on the SpiNNaker2 system. The PhD student will participate in an international team in an EU-funded Doctoral Network project called MINDnet. The project consists of 15 PhD students at seven universities, one research center, and two companies, with partners from eight EU countries. All 15 PhD projects are within neuromorphic computing and analog signal processing, targeting applications in communication, sensing, geolocalization, space, and biomedical fields. This PhD project will take place at SpiNNcloud Systems, with PhD enrolment at TU Dresden. Apart from time at SpiNNcloud, there will be secondments of at least two months at the University of Pisa and Neurobus. Regular meetings with the other 14 PhD students in the doctoral network, including four training schools and two workshops, are planned.

Responsibilities and Qualifications

Combinatorial Optimization (CO) problems lie at the heart of many real-world applications, from logistics and scheduling to energy systems and transportation. Neuromorphic computing has emerged as a promising paradigm for energy-efficient and massively parallel computing inspired by the brain. This PhD project sits at the intersection of these two fields, exploring how spiking neural networks (SNNs) can be used as novel computational tools for solving CO problems, and how to accelerate such solvers on neuromorphic hardware. The project aims to gain an in-depth understanding of the potential of neuromorphic computing to serve as a new class of optimization solvers. The project will include the following tasks:

• Derive mapping strategies for classical optimization algorithms to SNNs, including the choice of neuron models and membrane parameters
• Benchmark neuromorphic implementations of optimization algorithms against classical solvers
• Explore gradient-based and gradient-free SNNs for solving optimization problems
• Apply findings to a real-world optimization problem at Neurobus

The doctoral candidate is expected to travel to network partners for two secondments, each lasting typically 2–3 months. The candidate is also expected to participate in outreach activities, including YouTube videos, social media updates, participation in public events, and dissemination to popular press. Due to the mobility rules of the Marie Skłodowska-Curie program, the applicant must not have resided or carried out their main activity in Germany for more than 12 months in the 36 months immediately before recruitment.

You must have a two-year master’s degree (120 ECTS) or a similar degree with an academic level equivalent to a two-year master’s degree.

Approval and Enrolment

The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the degree programs at the Technical University of Dresden, either in electrical and computer engineering or in computer science. For information about PhD studies and enrolment requirements at TU Dresden, please refer to the university information about PhD studies.

We offer

SpiNNcloud is a deep tech spin-off from the Chair of Highly-parallel VLSI Systems and Neuro-microelectronics at Technische Universität Dresden. We provide highly-parallel and real-time computing capabilities to empower customers with the third generation of AI-driven systems. SpiNNcloud has developed technology that combines statistical AI and brain-like computing to advance real-time AI applications to large scale with energy efficiency.

We offer a rewarding and challenging PhD position in an international team. With TU Dresden as a partner for PhD enrolment, we provide academic excellence in an environment characterized by academic freedom and collegial respect.

Salary and appointment terms

The salary will be competitive and aligned with the German public-sector pay scale TV-L E13 (PhD level), depending on qualifications and prior experience. The period of employment is 3 years and the start date is preferably in July 2026; starting after October 2026 will not be possible. The employment will be on-site in Dresden.

Further information

Further information may be obtained from Dr. Mahmoud Akl. If you are applying from abroad, information on working in Germany can be found on the Federal Government website and the International Students page of TU 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 considered. To apply, open the Apply now link, fill out the online application form, and attach all 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 follows 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).

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