Postdoctoral positions available

Fall 2016. The Bailey-Kellogg lab is seeking talented and motivated postdoctoral fellows to join us in designing, engineering, and analyzing next-generation vaccines and protein therapeutics. Open positions are focused on the development and application of computational methods to (position 1) drive the engineering of therapeutic proteins and (position 2) analyze rich data regarding immune responses to vaccination. The computational researchers in my lab work closely with experimental scientists in collaborating labs to put these methods to practical use. The labs are highly interactive, and interested researchers will be able to take advantage of "cross-over" opportunities bridging the approaches and applications.

Please email Chris Bailey-Kellogg, cbk -at- cs.dartmouth.edu, your CV, statement of research background and interests, and contact information for 2-3 references.


antibody and antigen

Position 1: Computationally-driven engineering of protein therapeutics

In close collaboration with the Griswold lab, we're developing and applying computational structure-based design techniques to engineer proteins for use in the treatment of infectious diseases, cancer, and chronic disorders (see e.g., PMIDs 25568954, 26000749, and 27334453). While the protein universe manifests a wide range of functions with potential therapeutic utility, clinical translation faces numerous hurdles, which computationally-driven engineering is helping to overcome. For example, we have shown that by targeted mutagenic elimination of immunogenic "hot spots" in a potent anti-staph enzyme, we can produce variants that escape immune recognition and are more efficacious in treating infections (including drug-resistant strains).

As our current projects expand their scope and new projects ramp up, funded by multiple NIH grants, we are seeking a postdoc to help develop and apply advanced computational methodologies to generate the next generation of protein therapeutics. Research opportunities span the full spectrum from computationally-driven discovery of new lead candidates to protein design-based enhancement and combination of existing ones, all requiring new computational methods tightly integrated with experimental evaluation. The ideal candidate will have a strong background in computational structural biology, including both algorithm implementation and practical use. Experience in computational protein design methods would be helpful, but is not required and can be learned in context.


antibody-ome

Position 2: Mining and modeling the "antibody-ome"

In close collaboration with the Ackerman lab, we're integrating computational and experimental techniques to uncover patterns that relate the biophysical features of antibodies with their innate immune effector functions, and to identify mechanisms that boost development of these properties in response to vaccination or infection (see e.g., PMIDs 25874406 and 26745376). The Ackerman lab has developed a novel high-throughput array platform that provides unprecedented data characterizing the antibody response. In a range of collaborations funded by the Gates Foundation and by an NIH program project grant, we're also working with other labs that produce complementary data on effector function, protection, and other immune response activity, from both vaccine trials and studies of natural infection. We then computationally stitch it all together into predictive, data-driven models.

As part of this effort, we are seeking a postdoc to help develop machine learning methods to analyze the rich, but sparse and noisy, data. This position will provide opportunities to integrate and analyze diverse data, to develop predictive models that will be used prospectively in vaccine development, and to optimize experiments to assess and improve the models. Since the focus of the position is the development and application of computational methods, key qualifications are a strong background in machine learning / statistical data analysis, along with data integration and visualization. Experience in computational immunology would be helpful, but is otherwise a benefit of this postdoctoral training.