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, we appreciate prior experience in implementing deep learning models for graphs and networks. Additional Information Benefits As a PhD candidate at UT, you will be appointed to a full-time position for
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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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equator to pole, from the continental shelf to the deep ocean and from the past to the present. The ocean is Earth’s largest reservoir of CO2 and heat; circulation, mixing, biogeochemistry and other marine
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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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, 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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-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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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
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. Upwelling events bring nutrient rich deep-water to the surface, promoting phytoplankton production through communities dominated by known producers of omega-3. As such, these areas could represent hot-spots