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Seeking an exceptional candidate to conduct research in human genomics, develop novel machine learning methodologies, and create programs addressing critical questions in gene regulation.This position is located at the NIH main campus in Bethesda, Maryland, U.S.A.
We are seeking a highly motivated individual to join our team specializing in human genomics and machine learning. In this role, you will be responsible for conducting cutting-edge research, devising innovative machine learning methodologies, and developing programs aimed at addressing critical questions in gene regulation.
Key Responsibilities:
- Conduct research in human genomics to advance understanding of gene regulation mechanisms.
- Develop novel machine learning techniques tailored for applications in human regulatory genomics.
- Utilize large-scale epigenetic and functional genomic datasets, genome-wide association studies, and population genetics data to construct predictive models of gene regulatory elements.
- Design and implement AI models to accurately identify disease-causing mutations in complex human diseases.
- Publish research findings in high-impact, peer-reviewed journals, and present results at national and international conferences.
- Collaborate with other NIH institutes and external research organizations to leverage complementary expertise and resources.
- Mentor postdoctoral and postbaccalaureate fellows to foster their professional development.
- Stay abreast of advances in AI, computational biology, and experimental techniques relevant to the field.
Position Requirements:
- Three or more years of pertinent postdoctoral experience with a robust publication record demonstrating significant contributions to research through peer-reviewed publications.
- Expertise in regulatory genomics, enhancer/silencer identification, disease genetics, population genetics, and/or functional genomics, coupled with experience and/or contemporary understanding of eukaryotic gene regulation principles.
- Proficiency in handling ENCODE, NIH Roadmap Epigenomics, and analogous datasets, along with demonstrated proficiency in classical machine learning and deep learning algorithms.
- Proven capability to apply mathematical modeling across a wide spectrum of challenges and fluency in Python and R, including proficiency in Tensorflow and PyTorch libraries, with familiarity with GPU-based computational architectures.
- Demonstrated ability to collaborate effectively on interdisciplinary projects, coupled with experience in mentoring and strong verbal and written communication skills.
The ideal candidate may or may not be a United States citizen.
Education Requirements:
Candidates must hold a doctoral degree in a quantitative field, such as Computational Biology, Computer Science, Bioinformatics or Mathematics, or related field.
Foreign Education: Applicants who have completed part or all their education outside of the United States must have their foreign education evaluated by an accredited organization to ensure that the foreign education is equivalent to education received in accredited educational institutions in the United States. We will only accept the completed foreign education evaluation. For more information on foreign education verification, visit the National Association of Credential Evaluation Services (NACES) website. Verification must be received prior to the effective date of the appointment.
Salary and Benefits:
This non-competitive appointment in the excepted service is like a federal full-time position. Salary will be commensurate with experience and qualifications. A full package of benefits is available.
How to Apply:
Prospective candidates are encouraged to submit their CV and Bibliography, accompanied by a cover letter detailing their research interests and proficiency in AI developments, along with the names of three references, to ovcharen@nih.gov. Please ensure to include the announcement number, NLM9491-2024, in the cover letter. Please refrain from including personal information such as birth date, social security number (SSN), or personal photograph in your application materials.
HHS, NIH, and NLM are equal opportunity employers.
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The research of the Ovcharenko research group focuses on deciphering semantics and studying the evolution of the gene regulatory code in eukaryotes.
With less than 2% of the human genome sequence being coding, the search for noncoding functional DNA is a guileless treasure hunt. We currently lack a fundamental understanding of the genomic language that governs the temporal and spatial dynamics of gene expression regulation, native to every cell of a living creature. In an effort to bridge the gap between modern success in genome sequencing and sequencing data interpretation, we are developing pattern recognition AI methods to functionally characterize noncoding DNA. Our ultimate goal is to use these methods to translate the noncoding genome sequence into function.
Understanding the gene regulatory landscape of the human genome will open doors for studies of population variation in noncoding functional elements, promoting identification of disease-causative mutations residing outside of genes. As mutations in gene regulatory regions might be mainly linked to an increased susceptibility to disease, not necessarily resulting in a disease phenotype, our research has the potential for mapping key regulatory elements in the vicinity of disease-linked genes. Availability of computationally defined datasets of human regulatory elements tailored to specific common diseases (including heart disease, obesity, diabetes, and cancer) will permit designing novel disease susceptibility measurement methods, expressly targeting selected elements.
We utilize AI (including Deep Learning), comparative genomics, Bayesian statistics, multiple sequence alignments, libraries of transcription factor binding sites, gene expression data, population genetics, and transgenic animal experimentation (the latter through collaborations) -- all to infer the noncoding genome function through the analysis of sequence data and evolutionary trends. Our research relies on collaborative studies with several research and clinical groups within the NIH and from other research universities and institutions.
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