Computational Biology: Immunology Multi-Scale
University of Nebraska
Computational and Systems biology group at the University of Nebraska (Lincoln, Nebraska, USA) is looking for a quantitatively trained and highly motivated post-doctoral researcher to expand and validate a virtual immune system: a mechanistic, multi-scale, and multi-cellular model of the immune system. In the model, immune cells are represented as discrete and autonomous entities in an agent-based framework in various compartments, while the extracellular environment is described as continuous concentrations by ordinary differential equations. Each cell is also modelled at additional molecular scales to capture the dynamics at the level of metabolism, gene regulation, and signal transduction, using various mathematical and computational approaches. In addition to life sciences research, we also develop technologies (such as Cell Collective) to make computational systems biology broadly accessible to users with varying computational background. More information about our group at helikarlab.org.
The researcher will work in collaboration with other members of the group, and will have the opportunity to work on multiple projects in an international and a highly interdisciplinary setting with laboratory scientists, computer scientists, engineers, and mathematicians.
The University of Nebraska has an active National Science Foundation ADVANCE gender equity program, and is committed to a pluralistic campus community through affirmative action, equal opportunity, work-life balance, and dual careers.
- Essential qualifications and skills include:
- PhD in a quantitative subject such as computational biology, bioinformatics, engineering, mathematics, physics, and computer science, or equivalent.
- Deep understanding of immunology.
- Mastery of modelling frameworks (at least one of differential equations, agent-based modelling, logical modelling, and constraint-based modelling) and their use in multi-scale modelling of complex biological systems.
- Strong expertise in parametric estimation.
- Computational and programming competence (e.g., C++, MATLAB, and LINUX).
Qualifications and Skills Include:
- Strong biology background (e.g., metabolism, signal transduction, immunology, host-pathogen interactions).
- Experience with extracting and manipulating large datasets from various databases, and integration of these data with predictive computational models.
- Experience with statistics and machine learning, especially in the context of multi-omics.
- Experience collaborating with wet-lab researchers
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