Research Associate I (Xu Lab) - CBD - School of Computer Science

Carnegie Mellon University•Pittsburgh, PA

11h

About The Position

Carnegie Mellon University is a private, global research university that challenges the curious and hardworking to deliver work that matters. Our extraordinary institution has distinctive areas of excellence and a culture marked by ambition and a deep, practical engagement with challenges facing society. We continue to produce talented alumni and draw faculty and staff eager to be a part of the university’s creative, dedicated and close-knit community. We place emphasis on practical problem solving, interdisciplinary learning, a transformative spirit, and collaboration. The Ray and Stephanie Lane Computational Biology Department (CBD) is renowned for its interdisciplinary approach to studying biological systems using computational methods. Faculty and researchers collaborate across fields such as computer science, biology, and statistics to address complex biological questions. Their work spans diverse areas including genomics, bioinformatics, systems biology, and computational neuroscience, driving innovation at the intersection of computation and life sciences. We are searching for a Research Associate. The position will assist in statistical modeling of single-cell multinome data as well as manuscript preparation. Flexibility, excellence, and passion are vital qualities within CMU. Inclusion, collaboration and cultural sensitivity are valued proficiencies at CMU. Therefore, we are in search of a team member who is able to effectively interact with a varied population of internal and external partners at a high level of integrity. We are looking for someone who shares our values and who will support the mission of the university through their work. Are you interested in an exciting opportunity with an exceptional organization?! Apply today!

Requirements

Master's Degree in a related quantitative field such as computational biology, computer science or a related discipline
1 year of hands-on experience in working with data
Experience developing algorithms
Successful background investigation may be required

Responsibilities

Developing algorithms to link enhancers with their cis-regulatory gene targets
Simulation studies and real-data benchmarking
Working with and analyzing biological data
Developing data
Implementing experimental protocols and documenting research
Conducting literature reviews to stay updated on the latest methodologies in biomolecular condensates