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differentially private learning, its connections to replicability of algorithms, and algorithmic fairness. Basic Qualifications Candidates are required to have a doctorate or terminal degree in Computer Science
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of Computation Group, seeks applicants for a postdoctoral fellowship to conduct research in differentially private learning, its connections to replicability of algorithms, and algorithmic fairness. Basic
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the unique nature of clinical medical data with low disease prevalence and difficulty obtaining ground truth data. Major regulatory science gaps include lack of methods for AI algorithm training with limited
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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. Developing and applying state‑of‑the‑art artificial intelligence and machine learning (AI/ML) algorithms to discover robust prognostic and predictive biomarkers, and design clinically actionable treatment
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 3 hours ago
the unique nature of clinical medical data with low disease prevalence and difficulty obtaining ground truth data. Major regulatory science gaps include lack of methods for AI algorithm training with limited
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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. Expertise is required or highly desired in one or more of the following areas: algorithms, analytical derivation, data analysis, coding, or mathematical modeling. Strong programming skills are highly desired
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application to lineage tracing Algorithms for characterizing structural alterations in bulk and single cell whole-genome data Mutational signature analysis for cancer/brain samples Analysis of repetitive
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees