Postdoc - Postdoctoral Fellow - Computational Genomics-1703114
AbbVie (NYSE:ABBV) is a global, research-based biopharmaceutical company formed in 2013 following separation from Abbott Laboratories. The company's mission is to use its expertise, dedicated people and unique approach to innovation to develop and market advanced therapies that address some of the world's most complex and serious diseases. AbbVie employs approximately 30,000 people worldwide and markets medicines in more than 170 countries.
Description
AbbVie is a global, research-based biopharmaceutical company formed in 2013 following separation from Abbott Laboratories. The company's mission is to use its expertise, dedicated people and unique approach to innovation to develop and market advanced therapies that address some of the world's most complex and serious diseases. Together with its wholly-owned subsidiary, Pharmacyclics, AbbVie employs more than 28,000 people worldwide and markets medicines in more than 170 countries.
At AbbVie, our vision is to be recognized as a biopharmaceutical company delivering a consistent stream of innovative medicines that solve serious health issues and have a remarkable impact on people’s lives.
To be successful, we need outstanding individuals willing to challenge themselves to find the best solutions for our patients. The AbbVie Postdoc program is one way we are doing just that.
Through our Postdoc program, we are hiring postdocs from key academic institutions for preferred areas of science in the U.S., while providing a unique opportunity for participants to build a solid career foundation in the pharmaceutical industry while building the AbbVie brand as an employer of choice for scientific talent.
The program offers a balance of structured learning and work experience, with accessibility to high-level knowledge building across the drug development continuum to help participants understand how everything fits together and is put into practice. It also provides them with a chance to establish working relationships with some of the world’s most respected scientists and leaders in the industry.
Participants in the Postdoc program play an integral part in our continued success and will help us to further grow as a leader in our industry.
Abbvie is making a significant push into Genomics and Genetics for drug discovery and development. In the past year we’ve made multiple investments in internal capabilities and external partnerships with leading academic groups and technology companies. In addition, we’ve recently created an internal research group, The Abbvie Genomics Research Center (GRC) within The Target Enabling Science and Technology Department (TEST). The GRC has core capabilities and research efforts in Genomic Technologies, Functional Genomics, Computational Genomics, Human Disease Genetics and Pharmacogenetics, and has dedicated support for IT infrastructure and access to excellent high-performance computing resources.
We are seeking a highly motivated Postdoctoral Fellow to be part of the GRC who will develop and apply computational and statistical methods linking gene-regulatory variation to disease relevant molecular mechanism (e.g. causal genes and pathways) and to integrate inferences of molecular mechanism with large-scale internal and external datasets describing clinical trial outcomes. The successful applicant will develop new methods and will apply and critically evaluate existing computational methods to address a core component of the GRC’s mission- to highlight drug targets with the highest probability of success for treating diseases.
The successful candidate will work in the integrative GRC where computational genomics researchers are physically integrated with experimental groups in newly renovated laboratory and office space. S/he will have access to a large internal high-performance computing cluster as well as external compute resources as needed. Novel computational results can be followed up in collaboration with researchers currently developing and applying state-of-the-art technologies such as CRISPR, RNAi, NGS, RNA Seq, and other genomic and proteomic platforms. S/he will be encouraged to exercise creativity, innovative thinking, and calculated risk-taking in conducting research projects in collaboration with a diverse community of scientists across multiple disciplines. Research results are expected to be published in high quality peer-reviewed scientific journals.
Key Responsibilities Include:
Independently design, execute and interpret critical experiments to answer scientific questions.
Understand the broad objectives of the project as well as their role in achieving those objectives, and modify experimental plan when required, to respond.
Learn, understand and master new experimental techniques.
Innovate and implement new experimental protocols.
Demonstrate a high level of proficiency in his/her field.
Set and meet experimental timelines.
Effectively organize and present scientific plans and data.
Contribute to writing and conceptual framework of publications, presentations, and patents.\
Routinely read and apply relevant scientific literature. Impact projects mostly through lab and/or pilot plant based activities where applicable.
Publish and disseminate data via external conferences and peer-reviewed publications.
Equal Opportunity Employer Minorities/Women/Veterans/Disabled
Requirements Basic:
• PhD in Statistical Genetics, Biostatistics, Statistics, Bioinformatics, Genetics, or closely related field, with expertise in model development and reproducible model comparison.
• The ideal candidates will have a high degree of statistical and mathematical competency with experience applying these skills to problems in genomics, pharmacology, or other relevant areas (e.g. demonstrated with relevant publications).
• Strong communication skills are essential as well as fluency in one or more or relevant programming languages, such as R, Python, Java, or C++Experience co-developing statistical models or algorithms as part of a team of highly active contributors.
• Experience co-developing statistical models or algorithms as part of a team of highly active contributors. Experience and comfort with collaborative tools such as shared git repositories and desire to keep code well organized, documented, and reproducible
Requirements (Preferred):
• Familiarity with Bayesian hierarchical models, Bayesian causal models, causal inference, and general machine learning techniques.
• Evidence of independent research capability including hypothesis development, experimental design and execution, data interpretation, problem solving, and project management
• Strong track record of high-impact scientific achievement including a robust publication record
• Proven track record of teamwork, adaptability, innovation, initiative, and integrity
• Should be ready to provide a narrative on why your academic training would qualify you for this position and why you have a sustained interest in this program
• Should be able to provide three letters of recommendation, one at least from your PI, and preferably, one from the Chair of your department or a member of the thesis committee
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