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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
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- Delft University of Technology (TU Delft); 16 Oct ’25 published
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transduction could enable a new class of transistor circuits as well as novel sensor and transducers. Your PhD project will focus on both the fundamental physics of these devices with modelling, fabrication, and
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transistor is still early investigation. P-type GaN transduction could enable a new class of transistor circuits as well as novel sensor and transducers. Your PhD project will focus on both the fundamental
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urban design, environmental stressors (e.g. heat, air pollution, pollen), and social factors (e.g. safety, norms, networks) influence active lifestyles; develop and test physical activity and destination
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, postdocs, and faculty members. Our group focuses on understanding and mitigating corrosion processes, and on the development of electrocatalysts and electrochemical sensors through the synthesis of materials
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, to mitigate user burden, monitoring should occur as minimally obtrusive and as engaging for a large audience as possible. Unobtrusive sensor technologies could complement self-reported data and may reduce the
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a PhD position in the field of metasurfaces for single-molecule biosensing. This project aims to develop a new generation of single-molecule sensors that exploit collective and localized resonances in
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electrical power, enabling smart sensors to operate without batteries. You will explore novel capacitor-based rectifier architectures, adaptive impedance-matching algorithms, and on-chip protection mechanisms
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photovoltaics (PV) was identified by the IPCC as the technology with the highest potential among emission mitigation options. Scientific studies modelling energies scenarios for net zero emissions by 2050 project
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? Then this PhD position is for you! Quantum technologies are based on uniquely quantum effects. Among them, sensors using oscillating mechanical systems are especially promising: they can advance
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. This simple unit is limiting the learning capabilities of recurrent neural network models in tasks characterized by multi-timescale and long-range temporal dependencies. To implement multi-scale adaptation, in