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Field
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developing and improving the Parcels-code.org code; working within a large, interdisciplinary project. At the end of the project, you will have: a deep understanding of the hydrodynamic processes that control
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the composition and functioning of microbial communities in environments ranging from the deep sea to large lake systems. Within this department a subgroup of organic geochemists is developing novel (analytical
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: Developing novel techniques to understand how information is processed within deep neural networks. Developing methods that achieve high accuracy while also being safe, interpretable, responsible, and reliable
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emerging as a promising paradigm to overcome bottlenecks in conventional computing, offering ultra-fast and low-energy information processing. Recent advances include both spiking and deep learning schemes
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-design. Experience with hardware acceleration (FPGAs, GPUs, SoCs) and low-power design. Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow) is a plus. Ability to work in an
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of the AMS Institute (Urban Energy, Metropolitan Food Systems, Mobility, Circularity, Climate Responsible Cities, Responsible Digitisation); A so-called T-profile, combining deep knowledge in the field
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, and optimum sampling strategies. Proficiency in machine learning, deep learning, and artificial intelligence techniques. Familiarity with clinical applications and workflows. Basic understanding
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scientific programmers is supporting our deep learning research for large-scale experimentation. It's an exciting and supportive place to work, and we are proud to say we have a diverse team and an open
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with low-data, sparse, or noisy datasets, typical in early-stage drug discovery. Technical skills: Proficiency in Python (required). Practical experience with machine learning or deep learning workflows
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designers of their workplace rather than passive recipients of noise measurements. The research will follow an iterative research and development process characterized by deep, on-site engagement with NPICU