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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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research contributions will include designing algorithms for concept and structure extraction, building neural/graph hybrid models for pedagogical reasoning, implementing ontology-alignment methods for cross
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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staff position within a Research Infrastructure? No Offer Description The selected candidate will develop innovative and efficient resource optimization algorithms, suitable for this context, with cost
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center, please visit http://csiss.gmu.edu/. George Mason University College of Science (Mason Science) is committed to advancing inclusive excellence and fostering an environment free from discrimination
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data-driven analysis algorithms for the assessment of thin-film solar cell fabrication processes within NOMAD Oasis installations. The team is responsible for the installation and development of (meta
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(http://vanallenlab.dana-farber.org/) to work on the analysis of new datasets generated in the context of multiple clinically oriented cancer sequencing projects in order help advance efforts
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, with possibility of renewal Appointment Start Date: Flexible; Spring or Summer 2026 Group or Departmental Website: https://spoglab.stanford.edu (link is external) How to Submit Application Materials
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in bioinformatics, computational biology or related subjects; should be familiar with state-of-the-art NGS tools, algorithms and pipelines for processing and interpreting omic-seq data sets, with Unix
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work