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require new mathematical machinery. LOGSMS will combine diverse tools from discrete mathematics, learning theory and machine learning, thus facilitating the design and analysis of such models. PhD position
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developing new explanation methods. This will involve using tools from mathematical machine learning theory to prove mathematical guarantees about the performance of such new explanation methods, as
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PhD position offers the opportunity to work in a cutting-edge experimental facility, addressing fundamental challenges in condensed matter physics. HFML‐FELIX builds on a strong background in cutting
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discrete-event systems, supervisory control theory, and formal methods to apply for the PhD position within the Supervisory Control group (see Group Supervisory Control ), which is part of the Control
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will join an interdisciplinary team of several PhD students and postdocs in ARCNL’s highly cohesive Source Department and have as an objective to design & execute experiments, and work with advanced
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between high- and low-density flows, and effect of submarine canyon dynamics such as tides, internal waves, nepheloid layers, and up- and downwelling. This body of work suggests that conventional
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of Technology is a leading international community where innovation in aerospace meets global challenges. Our support and scientific staff, including PhD candidates, postdocs, and students, largely work together
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investigate deep learning methods for local data augmentation and adaptive point density control, addressing the anisotropy and uneven sampling typical of urban LiDAR. You will work on a four-year doctoral
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) that synthesizes population counts, nest outcomes and survival across life stages. You will extend the IPM to become spatially explicit and link it to management options (e.g., land use, predator pressure food
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Job related to staff position within a Research Infrastructure? No Offer Description As a PhD candidate, you will develop new analytical theories and methods for complex systems of partial differential