Postdoctoral Researcher - Ophthalmology - Campello Lab [Req#: 975866, Position#: 134010]

Postdoctoral Research Fellow – Integrative Computational Biology for Retinal Aging and Disease. A postdoctoral research position is available in the newly established Campello Laboratory in the Department of Ophthalmology at UT Southwestern Medical Center (Dallas, TX). We are seeking an experienced, highly motivated, and independent computational scientist to lead the development of computational and systems biology approaches for studying retinal aging and age-related retinal diseases.

The Campello Laboratory investigates the molecular and cellular mechanisms underlying retinal aging and neurodegeneration, with a focus on how age-associated changes increase susceptibility to retinal diseases, including age-related macular degeneration (AMD). Our research aims to distinguish the molecular features of healthy aging from those that drive disease initiation and progression.

By integrating large-scale multi-omics datasets with experimental models and in vivo retinal phenotyping, we aim to uncover mechanisms of retinal aging and identify biomarkers and therapeutic targets to preserve vision. The successful candidate will play a central role in establishing the laboratory’s computational biology program.

Working closely with experimental scientists, they will develop innovative computational approaches to integrate diverse molecular datasets, including genomics, transcriptomics, proteomics, and spatial multi-omics. They will apply systems-level approaches to uncover molecular networks regulating retinal aging and neurodegeneration, identify disease-associated pathways and therapeutic targets, and generate experimentally testable hypotheses.

This position offers a unique opportunity to help build a multidisciplinary research program at the interface of computational biology, systems biology, and retinal neuroscience. The ideal candidate will be intellectually curious, highly motivated, and capable of leading independent computational research while collaborating effectively with experimental scientists to build a rigorous, innovative, and multidisciplinary research program.

Responsibilities

Lead computational analyses of large-scale genomics, transcriptomics, proteomics, epigenomics, and spatial multi-omics datasets. Develop and implement reproducible computational pipelines for data processing, quality control, statistical analysis, and visualization of high-dimensional biological datasets. Integrate multi-modal datasets using computational and systems biology approaches to uncover molecular networks driving retinal aging and neurodegeneration, identify biomarkers, predict disease trajectories, and discover candidate therapeutic targets.

Apply network-based approaches to investigate gene regulatory networks, signaling pathways, cellular interactions, and mechanisms underlying disease susceptibility and progression. Collaborate closely with experimental scientists to design studies, interpret computational findings, and generate biologically testable hypotheses.

Contribute to manuscript preparation, fellowship applications, and grant writing. Present research findings at laboratory meetings and national and international scientific conferences. Mentor junior laboratory members in computational biology and data analysis as the laboratory grows. Contribute to building a collaborative, rigorous, and reproducible computational research environment.

The ideal candidate will be intellectually curious, highly motivated, and capable of leading independent computational research while collaborating effectively with experimental scientists to build a rigorous, innovative, and multidisciplinary research program.

Responsibilities

Lead computational analyses of large-scale genomics, transcriptomics, proteomics, epigenomics, and spatial multi-omics datasets. Develop and implement reproducible computational pipelines for data processing, quality control, statistical analysis, and visualization of high-dimensional biological datasets. Integrate multi-modal datasets using computational and systems biology approaches to uncover molecular networks driving retinal aging and neurodegeneration, identify biomarkers, predict disease trajectories, and discover candidate therapeutic targets.

Apply network-based approaches to investigate gene regulatory networks, signaling pathways, cellular interactions, and mechanisms underlying disease susceptibility and progression. Collaborate closely with experimental scientists to design studies, interpret computational findings, and generate biologically testable hypotheses.

Contribute to manuscript preparation, fellowship applications, and grant writing. Present research findings at laboratory meetings and national and international scientific conferences. Mentor junior laboratory members in computational biology and data analysis as the laboratory grows. Contribute to building a collaborative, rigorous, and reproducible computational research environment.

Required Qualifications Applicants must have: A Ph.D. in Computational Biology, Bioinformatics, Systems Biology, Computational Genomics, or a related quantitative field, with demonstrated expertise in analyzing large-scale biological datasets. A strong publication record demonstrating scientific productivity and leadership in computational biology.

Extensive experience analyzing next-generation sequencing datasets, including bulk and/or single-cell genomics and transcriptomics. Demonstrated expertise in integrating multiple omics datasets using computational and statistical approaches. Experience with systems biology, network analysis, gene regulatory network inference, pathway analysis, or network medicine.

Strong programming skills in Python, R, or similar programming languages, and experience developing reproducible computational workflows for large-scale biological datasets. Strong statistical, analytical, and problem-solving skills. Excellent written and verbal communication skills. Demonstrated ability to work independently while collaborating effectively within interdisciplinary research teams.

A strong commitment to scientific rigor, reproducible research, and open science practices. The following qualifications are highly desirable but not required: Familiarity with machine learning or artificial intelligence methods for biomedical data analysis. Experience with cloud computing, GPU computing, or large-scale computational infrastructure.

Interested, qualified candidates should submit:

• A curriculum vitae, including a list of publications and technical expertise.
• A one-page cover letter describing previous research experience, scientific interests, motivation for joining the Campello Laboratory, and long-term career goals.
• Contact information for three professional references.