Staff Scientist

Department BSD MED - Genetic Medicine - De Jesus Research Staff

About the Department

The De Jesus Laboratory studies the role of RNA modifications, particularly N6-methyladenosine (m6A), in pancreatic islet biology and diabetes. This Laboratory combines molecular biology, genomics, bioinformatics, genetic models, and human islet studies to understand mechanisms regulating β-cell function, stress responses, and disease progression in diabetes. The De Jesus Laboratory develops and applies cutting-edge sequencing technologies, including single-cell epitranscriptomic approaches, to investigate gene regulation in endocrine cells.

Job Summary

The Staff Scientist will lead and support research projects focused on epitranscriptomic regulation of pancreatic islet biology and diabetes. The individual will design and execute experimental studies examining m6A-mediated gene regulation using biochemical, molecular, genomic, and computational approaches. Responsibilities include developing and optimizing sequencing-based assays, conducting bioinformatic analyses of large genomic datasets, mentoring trainees, contributing to manuscript and grant preparation, and supporting collaborative projects involving human pancreatic islets.

The Staff Scientist will play a central role in developing and implementing advanced methodologies including J2 immunoprecipitation sequencing (J2-IP-seq), m6A profiling approaches, single-cell m6A sequencing, and integrative multi-omics analyses.

Responsibilities

• Designs, develops, and implements experimental protocols related to RNA biology, epitranscriptomics, and pancreatic islet function.
• Leads projects investigating m6A regulation in β-cells and other endocrine cell populations.
• Develops, optimizes, and performs J2-IP-seq and related RNA sequencing methodologies.
• Develops and advances single-cell m6A sequencing approaches and associated analytical pipelines.
• Performs bioinformatic analyses of bulk and single-cell sequencing datasets, including RNA-seq, single-cell RNA-seq, and epitranscriptomic datasets.
• Integrates multi-omics datasets to identify molecular mechanisms relevant to diabetes and islet biology.
• Analyzes and interprets large genomic datasets and develops reproducible computational workflows.
• Collaborates with internal and external investigators on genomics and bioinformatics projects.
• Mentors graduate students, postdoctoral trainees, technicians, and undergraduate researchers.
• Contributes to manuscript preparation, grant writing, and progress reports.
• Presents research findings at scientific meetings and collaborative forums.
• Assists with laboratory management, research planning, and training activities.
• Serves as a resource for collecting data and performing analysis. Facilitates and promotes a research project by providing scientific or intellectual information.
• Develops laboratory protocols and training on new techniques. Manage, analyze and make recommendations on complex data sets for research.
• Performs other related work as needed.

Minimum Qualifications

Education:

Minimum requirements include a PhD in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through 5-7 years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

Experience:

• Background working with pancreatic islet research and diabetes-related studies.
• Background working with human tissue research and genomic technologies.
• Understanding of bioinformatics and statistical analysis of large-scale genomic datasets.
• Familiar with next-generation sequencing technologies and data analysis.
• Analyzing single-cell transcriptomic and epitranscriptomic datasets.
• Comfortable mentoring students and staff, a strong publication record, and a demonstrated commitment to excellence in research.

​ Technical Skills or Knowledge:

• Knowledge of J2-IP-seq, RNA immunoprecipitation methods, and sequencing-based technologies.
• Knowledge of single-cell m6A sequencing methodologies and data interpretation.
• Knowledge of molecular biology, RNA biology, and epitranscriptomic mechanisms.

Preferred Competencies

• Analytical skills.
• Problem-solving skills.
• Attention to detail.
• Organizational skills.
• Verbal and written communication skills.
• Work independently and as part of a team.

Application Documents

• Resume/CV (required)
• Cover Letter (required)

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When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.

Job Family

Research

Role Impact

Individual Contributor

Scheduled Weekly Hours

40

Drug Test Required

No

Health Screen Required

No

Motor Vehicle Record Inquiry Required

No

Pay Rate Type

Salary

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FLSA Status

Exempt

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Pay Range

$80,000.00 - $100,000.00

The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.

Benefits Eligible

Yes

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in theBenefits Guidebook.

Posting Statement

The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.

Job seekers in need of a reasonable accommodation to complete the application process should call View phone number on aiapply.co or submit a request via Applicant Inquiry Form.

All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.

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