Summary
Forschungszentrum Jülich is seeking candidates for a PhD position aimed at advancing neuromorphic computing systems for large-scale machine learning. As part of an international team in the EU-funded MINDnet project, the successful applicant will contribute to innovations that mimic the brain’s energy efficiency and computational capabilities.
PhD Position in Hybrid Electronic/Photonic Integrated Neuromorphic Computing Systems, Germany
Designation
PhD Candidate
| Field | Details |
|---|---|
| Research Area | Neuromorphic Computing and Analog Signal Processing |
| Location | PGI-14 Research Center, Jülich, Germany |
| Eligibility/Qualification | Master’s degree in Electrical/Electronic Engineering, Computer Engineering, Computer Science, Physics, or related fields |
| Job Description | Optimizing hybrid electrical–optical computing architectures, developing machine-learning models, experimental system setup, among other tasks. |
| How to Apply | Applicants should submit their CV, cover letter, and relevant certificates through the Forschungszentrum Jülich career portal. |
| Last Date to Apply | Open until filled. |
Research Area
The focus of this PhD project is the optimization of hybrid electrical-optical computing architectures and the development of technology-feasible learning rules tailored for machine-learning workloads. This will include benchmarking against state-of-the-art studies and conducting numerical modeling of brain-inspired algorithms.
Eligibility/Qualification
- Master’s degree in relevant fields.
- Experience with emerging memory devices.
- Strong electronics background.
- Proficiency in SPICE and related tools (LTspice, Cadence, MATLAB, Python).
- Excellent communication skills, team-oriented attitude, and strong command of English (at least B2 level CEFR).
Job Description
- Investigate and design optimal computing architectures for large-scale machine learning workloads.
- Characterize and model electronic and optical devices.
- Develop hardware-aware machine learning models and training methodologies.
- Conduct comparative benchmarking and performance analysis.
- Validate neuromorphic algorithms through numerical modeling.
- Participate in secondment opportunities and outreach activities.
How to Apply
Interested candidates should prepare and submit their application including:
- Updated CV
- Cover letter detailing motivation and relevant experiences
- Academic certificates/references
Applications should be submitted via the Forschungszentrum Jülich career portal.
Last Date to Apply
The position is open until filled. Interested applicants are encouraged to apply as soon as possible.
This scholarship opportunity aligns with cutting-edge research that could shape the future of AI and machine learning, encouraging candidates to bring their diverse backgrounds to an innovative environment.
