| Funding period: | April 1, 2025 to March 10, 2028 |
| Agency: | DFG |
| Funding scheme: | RTG |
| Further details: | https://grk2767.tu-dresden.de/principal-investigators |
| https://www.dfg.de/ | |
| Part of the following collaborative project: | Research Training Group RTG2767 - Supracolloidal Structures |
We acknowledge funding by the DFG RTG project "B13: Neuromorphic sensing via 2D-materials-nanoparticle networks" (NeuroSense-2D, grant agreement ID: RTG2767)
This PhD project focuses on machine learning-driven neuromorphic sensing using 2D material-nanoparticle networks. By combining supracolloidal assemblies with data-driven approaches, the research aims to develop adaptive, energy-efficient sensing systems inspired by biological neural architectures. Machine learning will be used to uncover structure-property relationships, optimize signal processing, and enhance sensitivity for applications in biosensing, artificial synapses, and neuromorphic computing. This work aligns with the RTG 2767’s mission to design and understand novel materials from the atomic scale to real-world applications.
| Funding period: | April 1, 2025 to March 10, 2028 |
| Agency: | DFG |
| Funding scheme: | RTG |
| Further details: | https://grk2767.tu-dresden.de/principal-investigators |
| https://www.dfg.de/ | |
| Part of the following collaborative project: | Research Training Group RTG2767 - Supracolloidal Structures |
We acknowledge funding by the DFG RTG project "B13: Neuromorphic sensing via 2D-materials-nanoparticle networks" (NeuroSense-2D, grant agreement ID: RTG2767)
This PhD project focuses on machine learning-driven neuromorphic sensing using 2D material-nanoparticle networks. By combining supracolloidal assemblies with data-driven approaches, the research aims to develop adaptive, energy-efficient sensing systems inspired by biological neural architectures. Machine learning will be used to uncover structure-property relationships, optimize signal processing, and enhance sensitivity for applications in biosensing, artificial synapses, and neuromorphic computing. This work aligns with the RTG 2767’s mission to design and understand novel materials from the atomic scale to real-world applications.
