Postdoctoral Fellow AI-Informed Bioengineered Models Predicting Human Response to mRN

About the job Postdoctoral Fellow – AI-Informed Bioengineered Models for Predicting Human Response to mRNA Nanomedicines and Vaccines

Location:

Gaithersburg, MD

Competitive Salary, Bonus & Benefits Package

Do you have expertise in bioengineered micro-physiological models, interest in artificial intelligence and passion for advancing innovative modeling technologies to push the boundary in mRNA therapeutics and vaccines? Would you like to apply your knowledge to impact discovery and development of genetic medicines in a company that follows the science and turns ideas into life-changing medicines? Then join the Vaccine & Regenerative Medicines team of Biopharmaceutical Development at AstraZeneca and unlock the power of what science can do!

AstraZeneca is a global, science-led, patient-centered biopharmaceutical company focusing on discovering, developing, and commercializing prescription medicines for some of the world’s most serious diseases. But we are more than a global leading pharmaceutical company. At AstraZeneca, we’re dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit.

Bring your expertise, apply your knowledge, follow the science, and make a difference. AstraZeneca’s Postdoc Program is for self-motivated individuals looking to deliver exciting projects in an encouraging, engaging, and innovative environment. You’ll work with an experienced AstraZeneca mentor, a premier academic co-mentor and a multidisciplinary scientific team assembled to support you and your project. Our postdocs are respected as specialists, encouraged to speak up, and supported to share their research at conferences, publish papers and achieve their career goals.

Through engagement in this 3-year program, you will have the opportunity to boost your career while contributing to making a difference to the lives of our patients!

In this exciting Postdoctoral Fellow position, you will work to develop a cutting-edge AI-informed bioengineered model for predicting human responses to genetic (e.g. mRNA) nanomedicine/vaccine candidates.

This project is supported by the Vaccine and Regenerative Medicine Drug Product Team within Biopharmaceutical Development Organization. The team’s purpose is to advance the development of mRNA drug products and processes through new technologies, including generation and screening of lipid nanoparticles (LNPs), to deliver life-changing vaccines and medicines to patients. This purpose is realized through convergence of a wide range of scientific subject areas including material sciences, bioengineering, and advanced data analytics and cross-disciplinary collaborations with multiple functions across AstraZeneca, including therapy Areas, Biologics Engineering and Data Science & Modeling.

You will be mentored and guided by Beata Chertok, a former academic professor (University of Michigan) and current Associate Principal Scientist/Team Lead in the Delivery of Intracellular Biologics at AstraZeneca – an inspiring leader in mRNA nanomedicines and a scientific authority in nanoengineering, targeted gene delivery and experimental modeling of nanomaterials behavior within biological systems. In addition, you will also receive academic co-mentorship from John Tsang, Professor of Immunobiology and Biomedical Engineering at Yale University – an extraordinary scientific authority in computational modeling of the immune system and a leader with vision in Human Systems Immunology.

Furthermore, you will be supported by our dynamic cross-functional team bridging wealth of expertise and experiences across multiple scientific domains, including data science, immunology, omics and bioinformatics.

You will use your biomaterials/tissue engineering expertise to develop a novel micro-physiological model that implements a unique bio mimetic path for rapid benchmarking of human responses to genetic vaccines and therapeutics. You will also contribute to establishing methodologies for collection and analysis of…