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science background, preferably in algorithm design, system programming, networking, operating system, and computer architecture. Experience in low-latency RPC, network stacks, distributed systems, host data plane
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in the inner ear and signal treatment algorithms assist in preparation and testing of the microelectrode arrays at the partner institution collaborate with an interdisciplinary research team in a
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-and-egg" problem of sampling: developing algorithms that learn to bias simulations automatically without requiring prior knowledge of the mechanism. You will contribute to open-source software
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. The PhD students will work on several tasks, including: development of safe data-driven control/reinforcement learning algorithms to recover parameter identifiability by exploration of different
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Join AI:GENOMIX, a newly established AI:X Labs at Aalborg University and help push the boundaries of how artificial intelligence can reveal the genetic architecture of common human diseases. As a
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with mathematical modeling and algorithmic insights Counterbalancing issues such as bias, overfitting, and inexplicable errors in excessive pre-training: by making systems explicitly aware of the reasons
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that predict genetic cancer risk and help explain these risks to people in a clear and safe way. One part of the project builds AI models that can predict the risk of several cancers at once and explain how
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pruning), carbon-aware computing, minimizing algorithmic complexity, maintenance requirements, mapping energy efficiency and related aspects using KPI (key performance indicators) with respect to ESG
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correction and/or mitigation. Knowledge about networking protocols and distributed algorithms. Experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid
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sensor integration. Experience with SLAM algorithms (vision-, acoustic-, or inertial-based), state estimation (e.g. Kalman filtering, pose graph optimization), or collaborative positioning is highly valued