Bioinformatician at the Postdoc and Instructor/Senior Staff level

Bioinformatician at the Postdoc and Instructor/Senior Staff level

The Peters and Sette labs at the La Jolla Institute for Immunology (LJI) have recently received multiple long term NIH grants to fund biomedical research with a significant component in bioinformatics starting in 2019. This has resulted in multiple opportunities to join our interdisciplinary team of scientists working towards understanding, preventing and curing immune related diseases including infections, allergies and cancer.

Applicants are encouraged to indicate one or more potential matches to the specific advertised positions in their application, but there is also the possibility to create custom positions for applicants that do not match the specific advertised positions but have outstanding skills in at least two of the following areas:
Software development (programming in Python preferred, or proficiency in other programming languages; Unix Shell; version control)
Immunology (especially adaptive immunity and epitope recognition)
Data analysis techniques (statistics, data visualization, machine learning)
Knowledge representation (database systems, RDF/OWL, OBO ontologies)
Experimental data analysis (RNA-Seq, flow/mass cytometry, single cell analysis)

At present, positions are available at the level of postdoc and above:
Postdoc positions are expected to be filled by candidates holding a PhD degree looking for advanced training positions, and will typically be for a period of 2-5 years.
Instructor/Senior Staff positions are expected to be filled by candidates that have postdoc (or comparable) experience, an outstanding track record, and can be permanent.

Specific positions:
-Knowledge representation in immunology (IEDB)
-Machine learning for epitope prediction (IEDB)
-Experimental data analysis: Immune system profiling in different types of Pertussis vaccinations
-Experimental data analysis: Immune correlates of progressing to active TB

How to apply: Interested candidates must include a cover letter, CV, and list of three references. Applications without a cover letter included will not be reviewed. Please apply here.

Background: IEDB
The Immune Epitope Database and Analysis Resource (IEDB) is a freely available online resource supported by NIAID. The IEDB ( contains data on B and T cell epitopes for humans, non-human primates, rodents, and other animal species from infectious diseases, allergens, autoimmune diseases, and transplantation. The affiliated Analysis Resource ( hosts tools to analyze IEDB data and predict T cell and B cell epitopes. Our goal is to continue to serve the scientific community by providing a one-stop resource to both catalog and analyze a wide-range of immunological and epitope related data.

Background: Pertussis
In the mid-1990s, concerns over vaccine-related side effects prompted the widespread replacement of the whole-cell Pertussis (wP) vaccine in favor of a safer acellular Pertussis (aP) vaccine, and recent years witnessed a worldwide reemergence of Pertussis (whooping cough) despite widespread vaccination. Our approach is to compare individuals born before 1995 and vaccinated in infancy with wP, with individuals born in 1996 or later and vaccinated with aP in infancy, thus directly studying the population and age group in which the increased disease incidence is noted. The goal of this proposal is the identification of the mechanisms and correlates of long-lasting vaccine-induced immunity associated with Pertussis vaccination and more generally defining the mechanisms of durable vaccine efficacy.

Background: TB
Almost 2 billion individuals are infected with Mycobacterium tuberculosis (Mtb) the causative agent of tuberculosis (TB). Despite the relative low lifetime risk of latently infected individuals developing active disease (~5-15%) it has been estimated that 80% of new cases of TB are due to reactivation of latent disease. To meet World Health Organization’s (WHO) goals of TB eradication by 2035 the massive reservoir of TB infection must be addressed. Our long term goal is to derive predictive models based on validated biomarkers that can then be developed into a viable, scalable, and efficient diagnostic test that can predict progression to active TB.