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The Talent Acquisition department hires qualified candidates to fill positions which contribute to the overall strategic success of Howard University. Hiring staff “for fit” makes significant contributions to Howard University's overall mission.

At Howard University, we prioritize well-being and professional growth.

Here Is What We Offer

• Health & Wellness: Comprehensive medical, dental, and vision insurance, plus mental health support
• Work-Life Balance: PTO, paid holidays, flexible work arrangements
• Financial Wellness: Competitive salary, 403(b) with company match
• Professional Development: Ongoing training, tuition reimbursement, and career advancement paths
• Additional Perks: Wellness programs, commuter benefits, and a vibrant company culture
  
  

Join Howard University and thrive with us!



Basic Function

Seeks applicants for a Research Scientist to conduct research in the field of Physics, with a particular emphasis on simulation-based inference. This will entail utilizing advanced computational techniques to analyze complex phenomena and make statistical inferences.

This position offers an exciting opportunity for a motivated individual to contribute to cutting-edge research in the field of Physics, leveraging simulation-based inference techniques to advance scientific knowledge and understanding.

Supervisory Accountability

This is a 2-year position with a renewal option after 1-year, contingent upon satisfactory performance and supervisor approval. This position is grant-funded.

Nature And Scope

The Research Scientist will be engaged in advancing the field of likelihood-free inference, particularly in the context of anomalous transport coefficients, by integrating machine learning techniques with Bayesian principles. The position will involve research activities aimed at scaling up likelihood-free inference methods, with a focus on quantifying anomalous transport coefficients.

Principal Accountabilities

• Lead research efforts to scale up likelihood-free inference techniques, utilizing normalizing flows and other relevant methodologies.
• Quantify anomalous transport coefficients using likelihood-free inference methods merged with machine learning and Bayesian principles.
• Initiate work on embedding networks and transfer learning models to enhance the accuracy and applicability of inference results.
• Participates in the development of manuscripts, presentations and conduct of primary and secondary data analyses.
• Performs other related duties as assigned.
  
  

Core Competencies

• Fields of specialization: Physics, Astrophysics, Mathematics
• Ability to develop and implement computational algorithms and methodologies for quantifying transport coefficients.
• Advanced knowledge of the principles and techniques of the subject discipline. Knowledge of modern research methods, data collection and analysis and skill in their application.
• Strong analytical skills and familiarity with statistical analysis methods.
• Excellent communication skills, including the ability to present research findings effectively in both oral and written formats.
• Demonstrated ability to work collaboratively with a diverse team of researchers and students.
  
  

Minimum Requirements

Successful applicants will have a Ph.D. in Physics, Astrophysics, Mathematics, or a related field.

Experience in likelihood-free inference, machine learning, or Bayesian statistics is desirable, but not required.

Demonstrated research experience in computational physics or related areas.

Ability to work independently and manage multiple tasks effectively.

Commitment to fostering an inclusive and collaborative research environment.

Note: This position description should not be construed to imply that these requirements are the exclusive standards of the position. Incumbents will follow any other instructions, and perform any other related duties, as may be required. The university has the right to revise this position description at any time. This position description is not be construed as a contract for employment.

Compliance Salary Range Disclosure

Expected Pay Range: $72,727 - $80,000

Seniority level

• Seniority level

  Entry level

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