Postdoctoral Position in Dr. Michael J. Betenbaugh's Lab

Description

Are you eager to tackle cutting-edge challenges at the intersection of computational modeling, data science, and bioprocess optimization? Join Michael Betenbaugh’s research lab to develop and refine some of the most sought-after research skills in both academia and industry.

We are seeking a highly motivated and skilled Postdoctoral Researcher to lead a project focused on data-driven modeling, AI-assisted bioprocess optimization, and database development for CHO cell bioproduction. This project will involve genomic-scale metabolic modeling, kinetic modeling, AI-driven analytics, and the creation of a structured database to enhance the understanding and control of monoclonal antibody (mAb) production in CHO and HEK cell lines. A Ph.D. in Chemical Engineering,  Systems Biology, Computational Biology, or a related field is desired.

Johns Hopkins provides an exceptional research environment, fostering innovation at the interface of biomolecular engineering, computational biology, and biopharma.

Why Join Us?

·         Collaborate with top-tier industry partners in biopharma and biotech through the Betenbaugh Lab’s extensive network.

·         Be part of AMBIC, a leading biomanufacturing center, where you will have access to conferences, workshops, and seminars (Prof. Betenbaugh serves as the Center Director).

·         Mentorship & career development – Regular one-on-one meetings with Dr. Betenbaugh and collaboration with experts in the field.

·         Competitive standard benefits, with a highly supportive and interdisciplinary research environment.

·         Lab alumni have secured positions at numerous current and emerging biotech startups.

·         Baltimore offers an affordable and vibrant East Coast city experience with access to premier research and healthcare institutions.

·         Initial appointment for one year, with an expectation of at least two years total.

Key Responsibilities:

·         Conduct literature reviews on genomic-scale metabolic modeling, kinetic modeling, and AI-driven bioprocess optimization.

·         Develop data-driven models to predict and optimize CHO and HEK cell culture performance.

·         Apply machine learning and statistical modeling techniques to enhance process control and decision-making in bioprocessing.

·         Perform flux balance analysis (FBA), kinetic simulations, and multi-omics data integration to refine metabolic models.

·         Design, develop, and maintain a structured database for CHO cell metabolism and bioprocessing data, integrating transcriptomics, proteomics, and metabolomics datasets.

·         Implement database management systems to improve accessibility and usability of CHO bioprocessing data.

·         Analyze and interpret large-scale bioprocessing datasets, ensuring data integrity and standardization.

·         Develop computational pipelines for automated data processing, metabolic flux analysis, and AI-assisted modeling.

·         Maintain detailed documentation of methodologies, workflows, and results, and communicate findings with industrial collaborators.

·         Prepare and present research findings through journal articles, conference presentations, and internal reports.

·         Assist in mentoring graduate students and junior researchers in computational modeling, data science, and database development.

Qualifications

• PhD in Chemical Engineering, Biological Sciences, Bioengineering, or a related field
• Background in  bioprocess development and biomanufacturing optimization
• Experience with mathematical modeling, and machine learning techniques.
• Proficiency in data analysis, statistical modeling, and experimental design.

·         Proficiency in Python and Matlab

Ability to work collaboratively in a multidisciplinary team environment.

Application Instructions

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