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learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential inference algorithms as well as proofs of their correctness and efficiency) and systems (e.g
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reliability of autonomous Guidance, Navigation, and Control (GNC) algorithms when subjected to realistic stimuli. This research stems from the legacy of the ERC-funded EXTREMA and MUR-funded COSMICA projects
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the Health Sciences Compensation Plan. UC Salary information can be found here: https://www.ucop.edu/academic-personnel-programs/compensation/2024-25-academic-salary-scales.html Application Window Open date
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on the development of algorithms for ECG signal analysis and validation of novel biodegradable ECG electrodes. The contract will be within the framework of the “Green Electrodes for Sustainable Electrophysiology
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algorithms. The post-doc will carry out theoretical work on causal abstraction and causal alignment, implement algorithms and experimental pipelines in Python/PyTorch, and run experiments on GPU clusters
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of elements) of the model.; 3) develop an optimization algorithm based on genetic algorithms and metamodels and 4) design functionally graded OC scaffolds using different biomaterials. The doctoral candidate
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monitoring. c. Develop custom algorithms, prompts, and inference workflows tailored to business requirements. d. Integrate AI services and APIs into existing systems and applications. e. Write clean, efficient
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testing of model-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs
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The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL). Northwestern, TTIC, and UIC. Position ID: Northwestern-The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL
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Operations Manager to lead and monitor operations for continuous algorithm improvement and validation. This will involve guiding the development of data management systems and establishing data assessment