Company Name:-
United Therapeutics

Job Location:-
Silver Spring, MD

Job Summary:-
Job detailsJob TypeFull-timeFull Job DescriptionWhat we do
United Therapeutics Corporation focuses on the strength of a balanced, value-creating biotechnology model.

We are confident in our future thanks to our fundamental attributes, namely our commitment to quality and innovation, the power of our brands, our entrepreneurial culture and our bioinformatics leadership.

We also believe that our determination to be responsible citizens — having a positive impact on patients, the environment and society — will sustain our success in the long term.

We currently have five approved products on the market and a long-term mission of providing an unlimited supply of transplantable organs for those who need them.

Our company was founded by an entrepreneur whose daughter was diagnosed with a life-threatening condition.

She sought to find treatment options and a cure for her daughter and patients like her.

We are founder-led, and relentless in our pursuit of “medicines for life”.

We continue to research and develop treatments for cardiovascular and pulmonary diseases, pediatric cancers and other orphan diseases.

How you’ll contribute
The Computational Lab for In Silico Molecular Biology (CLIMB) applies computational modeling to enable in silico design and testing of therapies for lung disease.

As part of this effort to develop an integrative multi-scale lung model, the Data Scientist – Computational Biology will analyze and integrate diverse data types, including molecular, imaging, and text-based (scientific publications) to map biological processes relevant to lung disease pathogenesis and treatment.

Analyze and interpret high-dimensional molecular datasets, both public and internal, to identify biological processes/pathways relevant to lung physiology and pathology and apply this information to discover drug targets and infer mechanism of action.

Conduct research into suitable machine learning approaches for deriving insights from molecular datasets through gene regulatory network inference, dimensionality reduction, clustering, or supervised learning.

Apply natural language processing techniques to extract relationships among biological processes from literature sources.

Integrate biological knowledge from diverse sour

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