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PhD Candidate for Neuromorphic Approaches to Solving Combinatorial Optimization Problems Using Spiking Neural Networks on SpiNNaker2(m/w/x)
Description
In this research role, you will bridge the gap between neuromorphic computing and optimization by developing SNN-based solvers and deploying them on the advanced SpiNNaker2 system.
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Upload your CV and Nejo AI will find matching job offers for you.
Requirements
- •Willingness to travel for secondments
- •Participation in outreach and dissemination
- •Compliance with MSCA mobility rules
- •Two-year master’s degree or equivalent
- •Completed master's degree before start
Education
Tasks
- •Derive mapping strategies for classical optimization algorithms to SNNs
- •Select appropriate neuron models and membrane parameters
- •Benchmark neuromorphic optimization implementations against classical solvers
- •Explore gradient-based and gradient-free SNNs for optimization
- •Apply research findings to real-world optimization problems at Neurobus
Tools & Technologies
Languages
English – Business Fluent
Benefits
Mentorship & Coaching
- •Interactions with doctoral network
Diverse Work
- •Interdisciplinary activities
Additional Allowances
- •PhD scholarship
Other Benefits
- •Rewarding and challenging position
- •Academic excellence
- •Academic freedom
Informal Culture
- •International team
- •Collegial respect
- Huawei Research Center Germany & AustriaFull-timeOn-siteNot specifiedDresden
- Huawei
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PhD Candidate for Neuromorphic Approaches to Solving Combinatorial Optimization Problems Using Spiking Neural Networks on SpiNNaker2(m/w/x)
The AI Job Search Engine
Description
In this research role, you will bridge the gap between neuromorphic computing and optimization by developing SNN-based solvers and deploying them on the advanced SpiNNaker2 system.
Let AI find the perfect jobs for you!
Upload your CV and Nejo AI will find matching job offers for you.
Requirements
- •Willingness to travel for secondments
- •Participation in outreach and dissemination
- •Compliance with MSCA mobility rules
- •Two-year master’s degree or equivalent
- •Completed master's degree before start
Education
Tasks
- •Derive mapping strategies for classical optimization algorithms to SNNs
- •Select appropriate neuron models and membrane parameters
- •Benchmark neuromorphic optimization implementations against classical solvers
- •Explore gradient-based and gradient-free SNNs for optimization
- •Apply research findings to real-world optimization problems at Neurobus
Tools & Technologies
Languages
English – Business Fluent
Benefits
Mentorship & Coaching
- •Interactions with doctoral network
Diverse Work
- •Interdisciplinary activities
Additional Allowances
- •PhD scholarship
Other Benefits
- •Rewarding and challenging position
- •Academic excellence
- •Academic freedom
Informal Culture
- •International team
- •Collegial respect
About the Company
SpiNNcloud Systems GmbH
Industry
IT
Description
SpiNNcloud Systems GmbH is focused on AI inference products, partnering with Engineering, Research, Marketing, and Sales to translate technical differentiation into customer value.
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