Postdoctoral Fellow

The Zwezdaryk laboratory at Tulane University is seeking a highly talented and self-motivated molecular biologist for a funded post-doctoral position. Dr. Zwezdaryk’s team integrates experimental and computational methodologies to design novel point-of-care diagnostics and uncover the molecular mechanisms driving specificity and sensitivity. Our groups consist of members from diverse fields, including molecular and cell biology, immunology, microbiology, neuroscience, computational biology, and statistics, providing an excellent environment for interdisciplinary research training. We utilize cutting-edge technologies such as CRISPR-Cas12a and padlock probes combined with innovative machine learning and AI approaches, to design novel point-of-care diagnostics. Prospective postdocs will have the opportunity to work on one or more funded projects and collaborate with multiple groups at national and international research centers.

• Direct, hands-on experience in common molecular biology techniques (PCR, transformation, electrophoresis, DNA/RNA/protein characterization);
• Experience in genetic modification, including DNA construct design and cloning;

  Experience in standard analytical techniques (spectroscopy, microscopy, etc.);

• Proven record of high technical achievement, as evidenced by publication record.
• Proficiency in scripting environments for statistics and data analysis (R/Bioconductor, Python) and familiarity with command line interfaces and the Linux operating system.
• Candidates should be scientifically curious and able to work independently.

REQUIRED EDUCATION AND EXPERIENCE:

• Ph.D.
• Research experience in the areas of bioengineering, cell and molecular biology, biotechnology, immunology, microbiology, biology, biochemistry, or biostatistics

PREFERRED QUALIFICATIONS:

• Experience in development or use of CRISPR-Cas technology.
• The ability to conduct creative, independent research.
• Experience in milestone-driven research.
• Strong written and oral communication skills.
• Strong work ethic.
• Demonstrated dedication to laboratory safety.
• Expertise in statistical methods and algorithm development for biological systems.
• Familiarity with machine learning, and deep learning techniques.