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, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
how circuit structure supports computation. Will lead research on one or more of the following areas: Automated proofreading & annotation at scale: Machine learning approaches for error detection, human
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., machine learning for quantum error prevention/mitigation/correction) Quantum machine learning Quantum cloud technologies We are actively involved in practical applications through partnerships with
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on physics analysis and artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision Higgs and Standard
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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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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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work to discover cell specific isoforms and relate them to pathological signatures and genetic risk factors in ALS patient tissue. You will apply supervised and unsupervised machine learning methods
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applied mathematics, physics, computer sciences or social sciences with outstanding skills in quantitative methods ; - Provable experience working with social media data, specially very large datasets (we
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needed. You have experience in advanced bioinformatic analyses of omics datasets, preferably single cell and/or long read RNAseq data. Demonstrable experience in advanced machine learning and/or high
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particular focus on applications relevant to the Arab world. The successful applicant will join a multidisciplinary research team working at the intersection of machine learning, algorithmic fairness, human