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under my supervision to support research related to AI-driven modeling of forestry systems, plant physiological processes, and ecological traits. The position will focus on developing computational models
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-lab.github.io ) to develop computational methods and collaborate with Dr. Tianlong Chen (https://tianlong-chen.github.io/) at Department of Computer Science at the University of North Carolina at Chapel Hill
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Richland College/Division College of Education Department CED Child Development Research Center Location of Vacancy Columbia, SC Part/Full Time Full Time Hours per Week 37.5 Type of Faculty Position Faculty
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Research Assistant Professor, Research Associate Professor, Assistant Professor, Associate Professor
Candidates Department EHH BSOM Pharmacology Department Homepage https://pharmacology-toxicology.ecu.edu/ Advertising Department PHARMACOLOGY &TOXICOLOGY Division ECU Health/BSOM Classification Title Research
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approximately August 1, 2026, and is for recent Ph.D. recipients. Applicant’s research interests should be aligned with RTG faculty: http://homepage.divms.uiowa.edu/~mftehrani/Faculty.html Teaching load
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scholarship programming across WashU colleges, schools and other units. Develop strong relationships with WashU departments, programs, schools and centers. Communicate with key partners, including, but not
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Smet ( https://desmetlab.sites.vib.be ) and F. Van Breusegem ( https://vanbreusegemlab.sites.vib.be ) have expertise in abiotic stress signaling and in advanced molecular profiling, including mass
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successful candidate will be coordinating patient samples and developing testing methods to quantify large scale bulk mechanics in blood clots both plasma and whole blood. The final salary offer may be
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salary scale(s) for this position: https://www.ucop.edu/academic-personnel-programs/_files/2025-26/represented-oct-2025-scales/t23.pdf The current minimum salary range for this position is $69,073
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics