Postdoctoral Research Associate - Chapel Hill, United States - HERC- Upper MidWest

Description

Vacancy ID: PDS004070


Position Summary/Description:

Applications are invited for a postdoctoral position in the Leiderman Research Group within the Mathematics Department at the University of North Carolina at Chapel Hill.

Our group is also affiliated with UNC's Carolina Center for Interdisciplinary Applied Mathematics, Computational Medicine Program, and Blood Research Center.

We are searching for a collaborative, eager postdoctoral fellow to work on computational models of blood coagulation and development of predictive algorithms.

The Leiderman Research Group is broadly interested in the biochemical and biophysical mechanisms of blood coagulation, bleeding, and clotting. We use a data-driven, interdisciplinary approach that blends our mathematical, computational, and statistical expertise with biology and biochemistry collaborations.

We work with numerical and analytic approaches to solve systems of ordinary and partial differential equations, sensitivity, and uncertainty analyses to assess our models, and a variety of current methodologies to estimate model parameters.

A major goal of our group is to elucidate mechanisms that drive phenotypic variability in bleeding and clotting disorders.

We are looking to hire a postdoctoral fellow interested in learning about and developing mathematical models of blood coagulation and building predictive algorithms based on these models, proteomic data, and clinical data.

The desired candidate will drive research forward into independent lines of inquiry, help mentor graduate and undergraduate students, and interact with other members of the blood coagulation community.

Teaching within the Mathematics Department is not required but can be arranged if desired.


We Value:
Curiosity: We encourage and support a culture of intellectual curiosity among our group members. We foster an environment where individuals are empowered to ask questions, explore new ideas, and engage in continuous learning.


Collaboration:
We emphasize collaborative efforts, promoting interdisciplinary approaches to problem-solving. We work in a team-oriented atmosphere where open communication and knowledge-sharing are valued.


Diversity/Inclusion:

We believe that a diverse and inclusive environment enhances the quality of research and scholarship and enriches the overall academic experience for everyone.

We celebrate and respect individual differences to create a richer and more innovative community.


Education and Experience:

Be fluent in a computational language/platform, e.g., MATLAB, python, fortran, C/C++ -Have expertise with machine and/or statistical learning and parameter estimation.

Have experience working with and/or developing mathematical models and their numerical solutions, e.g., solving systems of ordinary or partial differential equations.

Be interested in collaborating with biologists and biochemists throughout the experimental process. Be creative, collaborative, and a team player.