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include: CMOS-based neuron and synapse circuit design Low-power digital architecture for SNN processing On-chip learning mechanisms Integration with sensor interfaces for biomedical signal processing What
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such as the CowFIT 3D camera system through industry collaboration with VikingGenetics. This state-of-the-art system enables large-scale, non-invasive measurement of body weight in over 30,000 cows directly
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to achieve the project objectives Execute the most promising approach Use the existing- and developing system to benchmark electrocatalysts You must have a two-year master's degree (120 ECTS points) or a
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than RGB will be actively researched. Exploring 3D canopy modelling and plant growth dynamics for digital twin integration. Self-supervised learning will generate multi-modal agricultural pre-trained AI
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power. Your primary tasks will be to: Develop a detailed 3D multiphysics model of the HT-PEMFC stack to analyze and optimize thermal management. Design a heat recovery system, tailored
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communication skills and willingness to work independently as well as to collaborate with your colleagues and peers. Commitment to complete the PhD coursework (30 ECTS points) and contribute to teaching
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. The research should focus on low-power embedded systems, multimodal sensing (including wearable shoe-based platforms), and edge-cloud computing with serverless and federated learning techniques. You will work
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supervision at the department to get teaching experience Qualifications You have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree