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— systems. It targets pioneering an architecture optimized for bilateral sensor processing. By fusing multiple data streams, VEST-CHIP aims to improve health tracking accuracy and unlock capabilities such as
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large organization preferably the financial sector or the health sector. Particularly, the research in the position is devoted to the role of IT architecture in enabling digital platforms and digital
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ultramafic rock systems. The position is associated with ongoing research aimed at advancing understanding of mineral fluid interaction processes and their impact on subsurface architecture and petrophysical
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hand and body configurations, surfaces, graphs and in permutations of inputs. Prominent architectures - including Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs) - can be viewed as
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. Typical research questions could be: How can tacit systems-architectural knowledge be captured and shared between humans and with machines? How to effectively collaborate on an architectural level with AI
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combined with AUV operations. Samples from collected sediment cores will be analysed onshore for various physical and chemical characteristics to help constrain the 3D stratigraphic architecture and
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be analysed onshore for various physical and chemical characteristics to help constrain the 3D stratigraphic architecture and compositional variability of the deposit. The datasets will be used
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observing system can help to build data-driven ML forecasts. The tasks of the PhD fellow will be to design the architecture and train a generative emulator of the coupled ice-ocean-biogeochemical model
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candidate will work with open available datasets obtained in rodents and unique datasets of neural activity. Your primary focus will be to design new learning frameworks and neural network architectures
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optimizing compilers, the classical and quantum fragments are separated in efficient implementations adapted to the changing QPUs and GPUs architectures. The candidate will work at the intersection