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Field
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grid analytics, and scientific imaging. The successful candidate will design and implement sparse algorithms for large-scale scientific and numerical computations. This role offers an exceptional
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The successful candidate will be responsible for developing computational and systems biology approaches to analyze spatial omics data and single-cell omics data (e.g., scRNA-seq, scATAC-seq, single
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genetic knockouts in yeast and mammalian cell lines, and protein purification. Job Responsibilities: 35%: Computational algorithm development and data analysis 35%: Design and conduct experiments with yeast
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vision, controls, cyber-physical systems and their security, hardware security, and machine learning and their security. The work will include algorithm design, prototype implementation (e.g., in Matlab
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creating the fastest algorithms for modeling of entanglement purification circuits; creating the highest performance quantum error detection circuits; developing symbolic computer algebra systems for quantum
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computer science, operations research, applied math, statistics, or a related field Strong background in machine learning, optimization, and/or algorithm design Excellent written and verbal communication skills
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interaction physics, precision calculations of (g-2) and PDF determinations, and the development of new algorithms, among others. Position Description The successful candidate will be expected to conduct
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Previous Job Job Title Post Doctoral Fellow - Computational Biology (Dr. Wan's Lab) Next Job Apply for Job Job ID 56921 Location Columbia Full/Part Time Full-Time Add to My Favorite Jobs Email this
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Ph.D. (or foreign equivalent) in Electrical Engineering, Computer Science, Applied Mathematics, or a closely
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writing C++ and PyTorch. Training and debugging RL agents. Imitation Learning algorithms for robotics or autonomous vehicles. Prior work combining RL with human data or feedback. A track record of code