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Field
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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statistical shape analysis, Riemannian geometry, time series and stochastic processes, and Bayesian statistics. Key responsibilities: To carry out research within the framework of the project, under
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of stroke patients and healthy volunteers. Developing algorithms for identifying and excluding motor unit filters associated to impaired motor units. Integrating real-time-decoded features of motor unit
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patients with cancer; to identify and validate predictive biomarkers of clinical outcomes in cancer; and perform meta- analyses using the Bayesian framework. The projects will lead to both collaborative and
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: ● Power module packaging and integration. ● High-density power converter design/prototyping, topology/loss analysis (including magnetic losses), thermal/EMI analysis. ● EMI emission and filter
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: ● Power module packaging and integration. ● High-density power converter design/prototyping, topology/loss analysis (including magnetic losses), thermal/EMI analysis. ● EMI emission and filter
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methodologies. Understanding of integrating Bayesian approaches in NN-based model Knowledge of model deployments to cloud platforms or past work with AutoML tools. Knowledge of MLFlow for maintaining model
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may include integrated adversarial detection, conformal prediction, and prompt filtering. Techniques relevant to safety and resiliency in autonomous vehicles may include optimal control, differential
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demonstrator sites where crushed basalt is being applied. The PDRA will use pH stat experiments with calcite seeds to assess the threshold of elevated alkalinity at which calcite precipitation occurs in filtered
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machine learning for transport simulation. A core innovation involves Bayesian metamodeling techniques to construct fast surrogate models of the simulation space, enabling efficient scenario analysis