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computational experts to conduct efficient analyses leading to discovery in mammalian neuron genome structure-function data. In parallel, trains to achieve mastery and excellence in running code written by other
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was launched in 2024 via the recruitment of 20 academic and 7 industrial PhD students. Over the course of the program, more than 260 doctoral students and 200 postdocs will be part of the graduate school
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(thedonnellycentre.utoronto.ca ). Required Qualifications: We are looking for postdocs that have excellent molecular biology skills and/or a strong computational background including machine learning approaches. Candidates should
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for massively parallel computers. Experience with quantum many-body methods. Preferred Qualifications: A strong computational science background. Familiarity with coupled-cluster method. Understanding
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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
or parallel reactors Collaborate with computational scientists to integrate machine-learning models for closed-loop materials discovery Collaborate with companion postdocs on functional materials
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software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
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and tool-using agents for experiment design, simulation steering, data collection, and lab/compute orchestration; planning and memory; multi-agent collaboration. Scientific Reasoning: Program/path
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critical zone functioning and guiding questions to understand it. The successful applicant should develop his/her program with this in mind. Other duties include advising students and postdocs, teaching