OPENINGS

openings

Post-Doctoral Fellowship:
Network Modeling Of Infectious Diseases

Applications are invited for a full-time postdoctoral position at the Department of Civil Engineering and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University under the supervision of Associate Professor Lauren Gardner. The applicant will be expected to undertake and develop research on the topic of spatial epidemiology, with a focus on the development of models to infer outbreak risk factors, predict outbreaks, and optimize resource allocation for outbreak mitigation. Candidates should have expertise in one or more of the following areas: network modelling, optimization, machine learning, statistical modelling, and data visualization, with previous experience working on epidemiological applications.

The postdoc will work closely with an international multidisciplinary team of faculty and Ph.D. students across Engineering and Public Health. In addition, the PI will make every effort to mentor the postdoc for transition into a faculty position. This includes guidance on grant-writing, teaching opportunities, and translation of research. Women and Underrepresented Minorities are highly encouraged to apply. This is a year-long postdoc that can potentially be extended up to two years upon satisfactory performance and availability of funding.

SELECTION CRITERIA

The candidate will be expected to:

  • Possess a PhD degree in computer science, engineering, applied or computational mathematics, or a closely related field.
  • Have expertise in one or more of the following areas: network modelling, machine learning, statistical modelling, data visualization.
  • Previous experience working on epidemiological applications.
  • Strong programming and data visualization skills.
  • Demonstrated experience in analyzing large scale data sets.
  • Demonstrated experience in working on large-scale multi-disciplinary projects.
  • The ability to work effectively as part of a multi-disciplinary research team.
  • Illustrate the motivation and discipline to carry out autonomous research.
  • High level interpersonal, written and oral communication skills in English.
  • A record of research accomplishment as reflected in publications in peer-reviewed journals and conferences and presentations at scientific meetings.

Start date is flexible. Review of applications will begin immediately and continue until the position is filled. Complete applications should include the following (in a single pdf file) to Lauren Gardner at l.gardner@jhu.edu:

  1. A cover letter
  2. A full curriculum vitae
  3. Up to two research publications and/or preprints
  4. The names and contact information for three references
  5. (Optional) A one-page original research proposal on the topic of your choosing, with the following headings: Motivation, Research Questions, Research Approach, Methods, Data Sources, Timeline.
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In radiation therapy with continuous dose delivery for Gamma Knife® Perfexion™, the dose is delivered while the radiation machine is in movement, as oppose to the conventional step-and-shoot approach which requires the unit to stop before any radiation is delivered. Continuous delivery can increase dose homogeneity and decrease treatment time. To design inverse plans, we first find a path inside the tumor volume, along which the radiation is delivered, and then find the beam durations and shapes using a mixed-integer programming optimization (MIP) model. The MIP model considers various machine-constraints as well as clinical guidelines and constraints.

Radiation therapy is frequently used in diagnosing patients with cancer. Currently, the planning of such treatments is typically done manually which is time-consuming and prone to human error. The new advancements in computational powers and treating units now allow for designing treatment plans automatically.

To design a high-quality treatment, we select the beams sizes, positions, and shapes using optimization models and approximation algorithms. The optimization models are designed to deliver an appropriate amount of dose to the tumor volume while simultaneously avoiding sensitive healthy tissues. In this project, we work on finding the best beam positions for the radiation focal points for Gamma Knife® Perfexion™, using quadratic programming and algorithms such as grassfire and sphere-packing.