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Computational epidemiology is a multidisciplinary field that applies novel computational methods for understanding and controlling the spread of human disease. It incorporates approaches and techniques from epidemiology, applied mathematics, computer science, artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and high-performance computing (HPC). It occupies the space between the computer scientists/mathematicians designing and building simulation software and epidemiologists who understand the public health domain deeply. It helps to inform decision-makers who want answers to pressing public policy questions now and in the future to move towards data informed policies and decisions.
MITRE's Biotechnology & Life Sciences Department is seeking a Principal Computational Epidemiologist to expand its expertise in this field, with a focus on MITRE's sponsors in the federal government (e.g., CDC, NIH, VA, ASPR). In this role, the candidate will be a key part of a MITRE teams conducting comprehensive planning and exploration across a variety of appropriate FFRDC sponsors to help drive scientific advancement that serves the public interest. The successful candidate will be a thought leader, empowered to shape and pursue a comprehensive vision of this area for MITRE and federal sponsors, with considerable autonomy and accountability. It is understood that as a non-profit, non-partisan entity working in the public good, MITRE uses data to make informed decisions and planning regardless of preconceived notions and informed by scientific causal rigor.
The ideal candidate will be a highly motivated individual with a track record of research excellence who enjoys having broad responsibilities, can thrive in a collaborative work environment, and can contribute to multiple projects across MITRE's work program. In particular, individuals with experience in studying or engaging in the following areas of work and research are encouraged to apply:
Development and refinement of synthetic populations to computationally model real-world conditions and possible scenarios.
Development and refinement of social contact networks and social network sensors.
Development and refinement of advanced models of infectious disease progression, including geographical components.
Development and refinement of advanced models for interventions, including public policies.
Leveraging real-time health surveillance data (including sampling) and social media for disease modeling, prevention, or reduction.
Prepare and provide presentation of results to a variety of scientific, public health, and healthcare audiences.
Specific duties may include the following:
Identify and help secure access to appropriate data sets - both public and private.
Lead the development of advanced algorithms and analytic models to predict the effects of major events (e.g., climate change, infectious disease, emerging pathogens) on human health, on one or more of the dimensions listed above, building on existing models and approaches.
Identify and pursue appropriate collaboration opportunities with external entities in academia or non-profit spaces outside of MITRE, as well as internal MITRE entities.
Innovate on the use of data and visualizations.
Establish a clear consensus with leadership and executives within MITRE regarding the utility of proposed analytic approaches.
Presenting and/or publishing externally.
Qualifications might include some or all of the following:
A graduate degree in an appropriate field, which may include epidemiology, data science, medicine, biology, computer science, mathematics, statistics, or equivalent. The successful candidate's credentials are likely to include several of these educational or professional areas.
Work and/or research experience with one or more forms of algorithmic development, machine learning, or AI applicable to this space.
Demonstrated experience in establishing successful collaborations across public and private entities.
Expertise in the use of cloud computing.
Relevant recent publications in peer-reviewed journals.
Experience working at, or with, NIH, CDC, WHO or well-respected academic or private industry organizations.
Experience working with publicly available socio-demographic data, including US Census data, local government, and publicly available geographic data.
This requisition requires the following clearance(s):
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