Assistant Professor of Computational Neuroscience (re-advertisement)

The position of Assistant Professor of Computational Neuroscience requires a strong foundation in both neuroscience and computational methods. Candidates should possess expertise in computational modeling, data analysis, and algorithm development relevant to understanding neural systems. Proficiency in programming languages commonly used in computational neuroscience, such as Python, MATLAB, or R, is essential. Experience with machine learning techniques, statistical analysis, and simulation of neural networks would be highly valuable. A solid understanding of neurobiology, neural coding, and systems neuroscience is critical to bridge computational approaches with experimental data. The role likely involves conducting independent research, publishing in peer-reviewed journals, and contributing to academic teaching and mentoring at the graduate and undergraduate levels. Collaboration skills and the ability to work in interdisciplinary teams combining biology, computer science, and mathematics are important. Candidates are expected to have a doctoral degree in neuroscience, computational biology, computer science, or a related field. Prior postdoctoral research experience in computational neuroscience or a closely related area is typically required to demonstrate research independence and depth of expertise. Familiarity with scientific software tools, high-performance computing environments, and data visualization techniques will support research productivity. Overall, the position demands a blend of theoretical knowledge, practical programming skills, and academic experience to advance the field of computational neuroscience through innovative research and education.