26 programming-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "https:" "https:" "Dr" "P" Postdoctoral positions at University of Miami
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and behavioral neuroscience techniques, and the ability to collaborate within a multidisciplinary research team. Experimental Research & Scientific Contribution Design, plan, and execute experiments
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program focused on molecular, cellular, and circuit‑level mechanisms relevant to brain function and neurological disease. This position requires a high degree of scientific independence, innovation, and
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assets. Department Specific Functions The Miami project to Cure Paralysis is a center for neuroscience regarding neurotrauma and neurological disease. Dr. Widerstrom-Noga’s research program focuses
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to apply for a faculty or staff position, please review this tip sheet . The T32 Training and Mentoring program brings participants to the Department of Psychiatry and Behavioral Sciences at the University
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, and brownbag. Actively participate in professional development programming related to research, teaching, and academic career preparation. Teaching and Curriculum Development Participate in teaching
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Functions Plan and conduct assigned and/or original research projects autonomously while working collaboratively with UM faculty, staff, postdocs and students. Perform basic molecular biology and cell biology
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used in neuroscience research. Ability to plan and conduct independent research, interpret experimental results, and communicate findings clearly. Commitment to scientific rigor, problem‑solving, and
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. Designs and writes programs to perform analysis and produces written reports on results. Collects pilot data to help support new and competing renewal applications. Supervises undergraduate and graduate
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science frameworks to public health programs addressing HIV, HCV, substance use disorder and homelessness. Participate in interdisciplinary projects that examine how policy, practice, and system-level
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Science, or a related field Strong programming skills in Python, R Solid understanding of: Machine learning fundamentals Deep learning architecture (e.g., CNNs, RNNs, Transformers) Optimization and model