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Job description
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JOB DESCRIPTION AND POSITION REQUIREMENTS
JOB DESCRIPTION AND POSITION REQUIREMENTS
Graduate Students are encouraged to apply for the Graduate Research Assistant opportunities with the National Synthesis Center for Emergence in the Molecular and Cellular Sciences (NCEMS) at Penn State.
The NSF funded NCEMS is seeking highly motivated graduate students (8 for Summer semester; 17 for Fall/Spring semesters) in programs such as Bioinformatics, Genomics/Computational Biology, Biostatistics, Data Science, Computer Science, Statistics, Biomedical Informatics, and related life science or engineering graduate programs, especially those with experience building reproducible computational workflows for high-throughput molecular datasets. This opportunity is offered at minimum Grade 15 half-time, with a willingness to match the candidate's current grade level pay at half-time if they are selected for the position, beginning in Summer 2026 or earlier, with appointments made on a semester-by-semester basis and the possibility of renewal across multiple semesters. Graduate Research Assistants will support one or more of the Center's Working Groups and will be mentored by designated NCEMS research staff.
The primary responsibilities of this position are:
Support community-scale synthesis research by contributing to one or more NCEMS Working Groups
Process and analyze molecular and cellular datasets, including transcriptomics and gene regulatory data such as ChIP-seq, ATAC-seq, and Hi-C
Develop, run, and maintain reproducible computational pipelines and workflows
Integrate and analyze multi-omics or multi-modal datasets for synthesis research
Contribute to additional project areas based on project needs, including methodological work in machine learning and scientific applications in proteomics, molecular dynamics, and evolutionary biology.
Produce and maintain well-documented code, notebooks, workflows, and other research outputs
Provide weekly progress updates during scheduled meetings with mentoring research staff
Prepare interim and final written reports or presentations as requested
Participate in regular meetings with mentoring research staff, broader NCEMS research personnel, and Working Group members to review progress, address blockers, and align work with project milestones
As part of this position, selected candidates will work under the supervision and mentorship of a designated NCEMS Staff member. Graduate Research Assistants are expected to work 20 hours per week in person in the designated NCEMS space in the Benkovic Building. Before the appointment begins, the selected candidate and mentoring research staff member will establish a fixed schedule that includes substantial overlap with the mentor's on-site schedule. All work performed during scheduled hours must be NCEMS-related and aligned with pre-approved project objectives, methods, timelines, and deliverables.
Applicants must be full-time doctoral students at Penn State University Park, be in good academic and conduct standing, and demonstrate strong interest in computational and data-driven molecular or cellular science research. Ideal candidates will possess strong communication, organization, and technical skills; the ability to work independently while seeking guidance appropriately; and the ability to collaborate effectively with staff, faculty, and interdisciplinary research teams.
Preferred qualifications include:
Experience with machine learning applications
Experience with multi-modal integration of common biological data types
Experience with workflow languages such as Snakemake or Nextflow
Proficiency in Python and R
Experience with transcriptomics and other gene regulatory data, including processing from raw files
Knowledge of basic applied statistics, including hypothesis testing and multiple testing correction
Minimum qualifications include:
Experience with Python or R
Familiarity with Linux
Project experience and interest in computational biology, bioinformatics, genomics, pipeline development, or related quantitative research areas
Interested students should submit:
A statement of interest and technical experience that briefly describes your relevant research or project background, including datasets, tools, programming languages, computational or analytical methods, data types, compute environments, and operating systems familiarity. A CV
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