Junior Research Assistant

Department

PSD Statistics: Administration and Staff

About the Department

The Department of Statistics of the University was established in 1949 to conduct research into advanced statistics and probability, to work with others in the application of statistics to investigations in the natural and social sciences, and to teach probability and statistical theory and practice on the undergraduate and graduate levels. The Department is home to world-class faculty who advance statistical, computational, and mathematical methodology across a broad range of scientific domains.

Professor Claire Donnat’s research group develops statistical and machine-learning tools for high-dimensional and spatial data, with a particular emphasis on applications in plant microbiology and microbial ecology. The group partners closely with wet-lab collaborators, providing a vibrant, interdisciplinary environment for quantitative scientists who want to see their work have direct biological impact.

Job Summary

We seek a Junior Research Assistant who will analyze large-scale biological data sets and help create open-source R and Python software to support our lab’s methodological research. Working closely with Professor Donnat, the assistant will preprocess raw omics data, conduct exploratory and multivariate analyses (e.g., PCA, CCA), and iterate on new algorithms for mutant detection and pathway discovery. The role is ideal for a recent master’s graduate who enjoys both coding and statistics and who is eager to learn cutting-edge methods in computational biology.

Responsibilities

• Clean, annotate, and batch-correct high-throughput sequencing and phenotyping data.

• Design and execute pipelines for mutant detection and pathway analysis (e.g., PCA, sparse CCA, eCCA).

• Perform rigorous QC and visualization to validate findings.

• Develop and maintain R and Python packages that implement lab methods; write unit tests and documentation.

• Automate data workflows using Git, CI, and reproducible-research best practices.

• Summarize results in figures, slide decks, and draft sections of manuscripts.

• Present progress at weekly group meetings and collaborate with graduate students and postdocs.

• Provide technical support for ongoing projects (hardware, software, data transfer).

• Maintains technical and administrative support for a research project

• Analyzes and maintains data and/or specimens. Conducts literature reviews. Assists with preparation of reports, manuscripts and other documents.

• Perform other related duties as assigned.

Minimum Qualifications

Education:

Minimum requirements include a college or university degree in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through < 2 years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

 

Education:

•  Master’s degree in Statistics, Computer Science, Bioinformatics, Computational Biology, or a closely related field by start date.

 

Experience:

• Coursework or project experience in multivariate statistics and/or machine learning.
• Proficient in R and Python for data analysis.
• Experience with biological or ecological data (e.g., RNA-seq, microbiome, metabolomics).
• Prior contribution to an open-source project or package.
• Familiarity with high-performance or cloud computing (Slurm, AWS, GCP).

 

Technical Knowledge or Skills:

• Background in high-dimensional or spatial statistics.
• Familiarity with tidyverse, Bioconductor, scikit-learn, and pandas.
• Comfort with Git and Linux command line.
• Strong quantitative reasoning and problem-solving ability.
• Excellent written and oral communication skills.
• Ability to manage multiple tasks and meet deadlines in a collaborative setting.

 

Application Documents

• Resume/CV (required)
• Cover Letter describing interest and relevant experience (required)
• Contact information for three references (required)