Sort by
Refine Your Search
-
, neurobiological, and clinical data to develop and validate predictive models of psychiatric risk. Prepare manuscripts for peer-reviewed journals and present findings at national and international conferences
-
Multiscale Immune Systems Modeling . This position focuses on the development, calibration, and analysis of multiscale agent-based models (ABMs) and differential equation models for Epstein–Barr Virus (EBV
-
: Engineering / Biomedical Appl Deadline: none (posted 2025/06/16) Position Description: Apply Position Description Chory Lab Seeking Postdoctoral Associate (Automated Evolution postdoc) This postdoctoral
-
, and behavioral measures. · Collect, process, and analyze multimodal datasets, including neural, physiological, and motion-tracking signals. · Develop and refine research protocols and methodologies in
-
Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks (PINNs), Biologically Informed Neural Networks (BINNs), and
-
develop novel computational approaches. Develop mathematical descriptions for the acquired data and work with our theorists collaborators to implement new theories. Integrate with the rest of the lab and
-
elusive, in part due to our limited understanding of the mechanisms of bnAb induction during natural infection. Dr. Williams has been awarded a NIH UM1 grant to develop new vaccine strategies that can
-
for projects examining questions in cardiovascular disease using extremely large data sets comprised of routinely-collected clinical data · Developing and maintaining requirements for access to Truveta Data and
-
invites applications for a full-time Postdoctoral Scholar to join an interdisciplinary research team studying environmental exposures and immune system development in children. The Scholar will work closely
-
policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics