Staff Scientist

Department

BSD OBG - Lengyel Lab

About The Department

The Kenny/Lengyel laboratory is part of the Department of Obstetrics and Gynecology/ Section of Gynecologic Oncology, studying the biology of ovarian cancer. The laboratory has about 15 members investigating the role of metabolism and methyltransferases in ovarian cancer metastasis. We use a variety of cutting-edge methods, including spatial proteomics, spatial metabolomics, 3D organotypic cultures of human tissue, spatiotemporal characterization of the immune system, and stable-isotype tracing in patients. Bioinformatic support and access to all Core facilities at the University of Chicago are available in the laboratory. We develop novel inhibitors to treat cancer and study biomarkers to diagnose ovarian cancer and detect ovarian cancer early.

Our translational research laboratory is in the Center for Integrated Science, a research building on campus that houses 40 independent research groups. This at-will position is wholly or partially funded by contractual grant funding, which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Job Summary

The job facilitates and promotes a research project or contributes to the scientific direction of a research resource. Receives a moderate level of guidance and direction. The focus of the job is to oversee a subgroup in the laboratory dedicated to projects using machine learning, data analysis, and artificial intelligence. The overall goal is to conduct multiple projects:

Textual Prediction of Survival (LLM classification & Attention Modelling)
This project develops a model to predict patient survival by analyzing heterogeneous clinical documents. Unlike traditional methods that rely on predefined predictive factors, it is designed to operate where risk markers are poorly understood, as is the case in ovarian cancer. The model integrates predictive accuracy with interpretability, offering individualized survival forecasts and treatment response predictions. Importantly, it highlights the textual features driving predictions, enabling discovery of novel risk associations for advancing precision oncology.

Multimodal Prediction of Response to Platinum-Based Chemotherapy (Computational Pathology & MIL)
This work combines histopathological hematoxylin and eosin (H&E) stained slides with clinical and demographic patient data to predict chemotherapy response. Using data from patients at the University of Chicago, the model will be validated in the Finnish DECIDER cohort of patients. Ongoing efforts aim to validate performance in the Australian AOCS cohort. The project demonstrates the potential of integrating multimodal data for improved clinical decision support.

Scanner Calibration (Computational Pathology & Omics normalization)
This project introduces a method to calibrate latent feature spaces of pathology foundation models to correct for scanner-induced variability. Without calibration, performance drops by ~15% across different scanners. Results will be demonstrated across 16 scanner pairs, 5 foundation model spaces, and 3 cancer/tissue subtypes.

Pathological Phenotypes Show Survival Groups (Clustering & Computational Pathology)
This study leverages advanced clustering methods to identify pathological phenotypes associated with survival outcomes in ovarian cancer. Findings show that stronger predictive models concentrate attention on phenotypes most linked to survival differences. Adding clinical data in a multimodal approach further sharpens these predictions by reducing noise and enhancing focus on biologically relevant regions.

Deep Visual Proteomics (Cell Segmentation)
This project advances the deep visual proteomics pipeline by integrating modern vision AI techniques to detect, segment, and extract specific tissue regions such as cell populations. Pathological slides (H&E or immunofluorescence) are processed, and selected cells or regions are laser-extracted into 384 wells for protein analysis. The pipeline will be fully integrated with QuPath, enabling visual verification by pathologists at every step. This ensures transparency and control, bridging computational methods with clinical workflows.

CODEX (Cell Segmentation, Cell Classification, Community Detection)
We analyze CODEX multiplexed imaging data using established, state-of-the-art computational pipelines to detect immunological differences between sarcomous and carcinomous tumors. It integrates methods for segmentation (InstanSeg), cell typing (MAPS), and neighborhood analysis (CytoCommunity) to process large-scale whole-slide images. The workflow generates millions of cell-level features, enabling deep biological insights while minimizing the need for custom tool development. Each step remains visually verifiable, ensuring confidence in results and flexibility for refinement with pathologist feedback.

The applicant will participate in grant writing, represent the laboratory at conferences, and provide project management for translational projects.

Responsibilities

• Supervise new laboratory personnel in the development subgroup.
• Lead collaborative efforts to optimize methods, modelling, and analysis.
• Serve as the lead contact for collaborations using AI.
• Compliance of research activities with institutional, state, and federal regulatory policies.
• Support the scientific writing of protocols (IRB), publications, and grants (both federal and non-federal).
• Contribute to proofreading, and fact-checking papers, grants, and presentations.
• Serves as a resource for collecting data and performing analysis. Contributes to facilitating and promoting a research project by providing scientific or intellectual information.
• Reviews laboratory protocols and training on new techniques. Manage complex data sets for research.
• Leads the contributions to scientific writing and publications, including protocols and grants.
• Trains new laboratory personnel.
• 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 2-5 years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

Education:

• PhD, MD, or equivalent doctoral degree and experience with pre-clinical and clinical studies.

Experience:

• Five years of relevant research experience. 
• A background in toxicology studies, clinical protocol development and execution. 
• Experience with basic statistics.

• Experience With Databases.

• Quality Control.

• Previous experience developing standard operating procedures.

Preferred Competencies

• Knowledge in one or more relevant scientific fields. 
• Proficient in research techniques or methods. 
• Knowledge of regulatory policies and procedures. 

• Analytical Skills.

• Problem-Solving Skills.

• Attention To Detail.

• Organizational Skills.

• Verbal and written communication skills. 
• Work independently and as part of a team. 
• Proficient in Microsoft Office.  

Working Conditions

• Lab Environment.

Application Documents

• Resume (Required)

• Cover Letter (Preferred)

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

Yes

Health Screen Required

Yes

Motor Vehicle Record Inquiry Required

No

Pay Rate Type

Salary

​Flsa Status

Exempt

​Pay Range

$70,000.00 - $90,000.00

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 the Benefits 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 773-702-5800 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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