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trends, and their future developments come with deep uncertainties. Infrastructure policies must account for these uncertain drivers and their dynamic interaction with changes in intended use. As part of
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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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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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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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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