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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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-cell and spatial-omics research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic
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developing novel methods and algorithms for applications in the Engineering Design domain; demonstrating a commitment to deliver results; working on research proposals; and working in a team environment. Job
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research associate position in AI for science. The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran
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of computational biology, Genomics, machine learning, and data science, contributing to the development and evaluation of advanced algorithms for analyzing large-scale biological datasets. This role is ideal
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beyond traditional error estimators, creating physics-based adaptation algorithms that intelligently predict where refinement will be most beneficial for smarter, more efficient simulations. We seek
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on developing and analyzing algorithms, building reproducible computational studies, and disseminating results in leading venues. The position collaborates with faculty, students, and external partners
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interactions, nuclear structure and reactions, electroweak structure, and lepton-nucleus scattering. The candidate will contribute to advancing statistical and computational algorithms to extend the capabilities
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maintaining supplies and reagents, isolating cells, preparing NGS libraries, performing standard QC, and preparing libraries for sequencing. Implement algorithms and computational tools for omics-based data