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demand. However, demand drivers are manifold, including technology advancements, population and economic trends, and their future developments come with deep uncertainties. Infrastructure policies must
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theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be opportunities to present at leading
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-based infrared sources is emerging as a promising and effective tool. In particular, the newly developed high-power intrapulse difference frequency generation (IDFG) based sources extend deep into the mid
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large-scale neural models of the early visual system. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience and strong deep learning
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researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be
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) developing and validating preprocessing pipelines; (3) architecting and comparing spectral-only and multimodal (HSI + NIR + Raman + RGB) deep-learning models; (4) implementing robust sensor-fusion strategies
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particular in deep learning, LLM, digital hardware design, embedded systems, audio processing; Proficiency in deep learning frameworks (e.g. PyTorch) and programming skills (SystemVerilog, Verilog, Python, C
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chemical reaction networks with robotic systems and analytical science. You will also learn how to programme robotic systems and how to implement aspects of deep learning and neural networks for reservoir
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PhD: A deep dive into youth cyberhate Faculty: Faculty of Social and Behavioural Sciences Department: Education & Pedagogy Hours per week: 28 to 40 Application deadline: 5 September 2025 Apply
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models. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience a and strong deep learning programming skills. Ability to work in an