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research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu/ . For questions, please email mweber@seas.harvard.edu Applications will be reviewed on a
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Eligibility criteria Instrumental optics and imaging (microscopy, camera detection) for biology. Skills in coding and experiment control. Basics of machine learning and/or signal processing. Teamwork
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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scientific papers Collaborate with neuroradiologists and neurologists in satellite projects Where to apply Website https://jobs.vhir.org/jobs/6646119-postdoctoral-researcher-neuroradiology Requirements
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with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental design
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professionals. Together, we’re unlocking the fundamentals of biology and building an open, inclusive future for science. We have an outstanding opportunity for a Postdoctoral Scientist to join the lab team of Dr
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 3 hours ago
Posting Details Posting Details Vacancy Number PD-0029 Working Title CSE Postdoctoral Scholar - (Pooled) Position Number PD0033 Location of Workplace Main UNCW Campus Brief Summary of Work for this
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work can be found by visiting https://federlab.github.io/ About the Postdoctoral Scientist role: This role is an ideal fit for an intellectually curious, creative, and driven scientist ready to gain
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genome-resolved multi-Omics methods, statistical/metabolic modeling, and machine learning. The postdoc will apply these approaches to generate a systems-level understanding of microbiomes including