Postdoctoral Fellow Computational Immuno-Oncology

Redwood City, CA, US
Oct 12, 2018
Required Education
Position Type
Full time
The Postdoctoral Program is designed for true investigational and experimental research. Participants will be mentored by renowned industry scientists and collaborators at AbbVie and focused on delivering cutting-edge advancements in Discovery, Development Sciences and BioPharma. The enriching training program offers a balance of structured learning and work experience which fosters a learning environment to advance individual development with accessibility to high-level knowledge building across the drug development continuum. This assignment is expected to be for two years minimally and no more than three years.

Technical expert that will investigate, identify, develop and optimize new methods/ techniques to address critical project needs. Continuously seek to improve existing laboratory methods and processes. Read and adapt literature to accomplish assignments. Demonstrate mastery of broad range of experimental techniques and methods of data analysis.


Checkpoint inhibitor-based immunotherapy is revolutionizing the treatment of several neoplastic conditions such as melanoma and lung cancer, however its efficacy in other cancer types remains limited. The development of more effective immunotherapy treatments is hindered by incomplete knowledge of the genetic mechanism governing host-tumor interaction. The cellular and molecular characterization of human tumor samples by high-throughput and deep phenotyping approaches have defined the important concept that the presence of an active immune microenvironment is associated with favorable prognosis and responsiveness to immunotherapy. Several observations suggest that three immune landscapes best define distinct varieties of the cancer microenvironment: an immune-active, an opposite immune-deserted and an intermediate immune-excluded. Although across cancers, and among subtypes, the prevalence of each landscape may differ, nevertheless, this trichotomy is observable across most solid tumors suggesting that convergent evolutionary adaptations determine the survival and growth of cancer in the immune competent host leading to predictable patterns determined by uniform immunological principles independent of the biology pertinent to distinct tumor tissue of origin. It is therefore reasonable to postulate that the mechanisms leading to cancer resistance to checkpoint blockade are similar across cancers deriving from different tissues. The objective of this study is the definition and application of integrative Computational Systems Biology approaches to characterize the genetic determinants of immune phenotypes at pan-cancer level. We hypothesize three main factors that influence the immune response to cancer. The first one is defined by the genetics of the tumor. The second vector consists of the genetic make-up of the subject bearing the disease, and the third one is represented by environmental factors. Furthermore, each factor is influenced by the convergence of different components adding further complexity to the model. The simultaneous evaluation of all these components represents a systematic approach, but poses several challenges, especially from a computational point of view, since it requires the synchronous collection of a wide diversity of samples and extensive clinical, phenotypic, and demographic data that are not easily integrated. The availability of large scale dataset at using different molecular profiling platforms can such as the TCGA help to identify novel relationships between cancer subtypes as well as correlation between immune phenotypes and the molecular features affecting immune response. The candidate will be involved in a large scale research program in Computational Biology to develop and apply state of the art data mining and machine learning methodology to stratify patients according to their immune phenotypes and to identify the main genomic variables that affect and/or predict such phenotypes and response to therapies.

Key Responsibilities:

  • Analysis - Interrogate large scale -omics data from both pre-clinical models and clinical samples in support of new therapeutic discoveries for the treatment of cancer.
  • Build - through your own efforts or by working with software engineers develop world-class bioinformatics capabilities (genomic databases and analytics software/pipelines) to support a dynamic oncology pipeline.
  • Collaborative Design - you should find it inspiring to work with a wide variety of colleagues (e.g., biology, chemistry, business); understand their needs and then propose and build solutions
  • Presentation - use your strong computational and data visualization skills to bring together disparate data types in compelling visualization packages that provide end users with the ability to see both 'the landscape' and details leading to improved decision-making
  • Adaptability - given our diverse mission you should be able to move fluidly between different project responsibilities
  • Breadth - as a talented and motivated Computational Biology Data Scientist you can contribute to a wide variety of missions related to enhancing drug discovery
  • PostDocs are expected to work in research teams, to be member of the scientific community by publishing in top-tier conferences and journals, and collaborate with peer researchers and software engineers.

  • PhD. Degree in Computational biology, Bioinformatics, Computer Science or similar field with a thorough theoretical and practical understanding of this field.
  • Graduate of accredited and nationally ranked university.
  • Record of publication in a prestigious journal(s).
  • Excellent problem-solving skills including critical and analytical thinking.
  • Excellent communication, leadership, and project management skills.
  • Demonstrated scientific writing skills and strong verbal communication skills.
  • Demonstrated ability to independently design and execute experiments, interpret data, and identify appropriate follow-up strategies.
  • Proven track record of teamwork, adaptability, innovation, initiative, and integrity.
  • Global mindset to thrive in a diverse culture and environment.
  • Ability to multitask and work within timelines.
  • Must be authorized to work in the Unites States
  • Expertise in computational biology/bioinformatics, genomic analysis, data and literature mining, disease and biological pathway and gene network/ systems biology analysis.
  • Strong data analytical skills, expertise in various bioinformatics software, databases, and programming packages including R, Shiny, Python or Perl.

Key Leadership Competencies:
  • Builds strong relationships with peers and cross functionally with partners outside of the immediate team to enable higher performance
  • Learns fast, grasps the "essence" and can change course quickly where indicated
  • Raises the bar and is never satisfied with the status quo
  • Creates a learning environment, open to suggestions and experimentation to drive the science in the field of interest
  • Embraces the ideas of others, nurtures innovation and manages innovation to reality


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