Summary

A newly funded postdoctoral position is available in the laboratory of Dr. Leo Luznik at Baylor College of Medicine. We are seeking a highly motivated and independent scientist with a strong background in cancer immunology and bioinformatics, particularly single-cell transcriptomic analysis and dimensionality reduction techniques such as tSNE.

Our lab is focused on understanding immune mechanisms underlying resistance and relapse in hematologic malignancies, with a goal of translating discoveries into clinical applications. We are part of a vibrant cancer immunology and immunotherapy research community and publish in top-tier journals.

The successful candidate will work in a diverse, collaborative environment where career development is a major priority. This position offers opportunities to lead high-impact projects, generate first-author publications, and apply for competitive extramural funding as a stepping stone to an independent research career.

Please send the following to Dr. Leo Luznik at [email protected]

• Current CV
• Contact information for three references
• A brief statement describing past research experience and future career goals

Job Duties

• Designs, executes, and analyzes advanced research experiments.
• Appliesbioinformatics tools to analyze single-cell RNA-seq and high-dimensional flow cytometry data.
• Conducts tSNE/UMAP clustering and trajectory inference to uncover novel immune cell states and mechanisms.
• Develops and optimizes new experimental protocols.
• Summarizes findings and contribute to manuscript preparation and grant applications.
• Mentors junior lab members and contribute to lab operations.

Minimum Qualifications

• MD or Ph.D. in Basic Science, Health Science, or a related field.
• No experience required.

Preferred Qualifications

• MD or Ph.D. in immunology, bioinformatics, computational biology, or a related field
• Strong data analysis skills with no required postdoctoral experience
• Hands-on experience with single-cell RNA-seq data processing and analysis pipelines (such as Seurat, Scanpy, etc.)
• Proficiency in R or Python for statistical computing and visualization
• Familiarity with dimensionality reduction techniques like tSNE, UMAP, PCA
• Experience with multicolor flow cytometry and cellular immunology techniques
• Interest in translational immunotherapy for leukemia and other hematologic malignancies
• Strong communication and writing skills, and a collaborative, team-oriented mindset