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& Research Learning about danger: a virtual reality approach to study learned predator recognition in fishes Yanell Braumuller PhD candidate Leiden University & Naturalis Biodiversity Center Rooting for
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infrastructures, rapid innovation cycles, and even autonomous deep space exploration. As Head of the In-Orbit Technology Demonstration Division, you will be at the heart of this transformation. You will lead ESA’s
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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for single-cell and spatial omics Deep learning and representation learning to model cellular states and interactions Explainable AI for biomarker discovery and patient stratification Cross-disease modeling
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-of-optica… Requirements Specific Requirements Masters degree in electrical engineering, applied physics, photonics or similar. Deep-tech understanding of semiconductor device physics, specifically regarding
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from the areas of few-shot learning, continual learning and modular deep learning, as well as different LLM alignment frameworks, based on reinforcement learning and direct preference optimisation
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languages, for example Python, and general purpose deep learning frameworks, such as Tensorflow or PyTorch; The interest and ability to share knowledge with other ESA organisational units. You should also
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of the following: Experience with Explainable AI. Experience with Deep Learning. An interdisciplinary background / interdisciplinary training. Have followed courses in Psychology or Philosophy
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Join an ambitious deep-tech venture backed by the European
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the next generation of managers as they investigate the opportunities presented by data analytics (machine learning, deep learning, data mining) and new information technologies (platforms, cloud computing