Postdoc positions in Computational Modeling at Morgan State University
Postdoctoral Fellow #1) Computational Neuronal Circuitry & Endocrine Modeling
Institution: Morgan State University, Baltimore, MD, USA
Project: Digital twins for the therapeutics of gestational diabetes (NIH-funded: OTA-26-004)
Position Summary: We are seeking a highly motivated Postdoctoral Fellow to contribute to the computational modeling of hypothalamic neuronal excitability and metabolic adaptation as part of an NIH-funded digital twin project. This research focuses on the complex neuronal networks modulated by metabolic and placental hormones during pregnancy. The successful candidate will drive a crucial phase of our multi-aim workflow, constructing mechanistic models that capture how circulating hormones alter the excitability of feeding circuitry to regulate glucose control and energy balance across healthy, obese, and diabetic maternal cohorts.
Required Qualifications
* Ph.D. in Applied Mathematics, Mathematical Biology, Computational Neuroscience, or a closely related discipline.
* Proven expertise in building mechanistic mathematical models of neuronal excitability (e.g., Hodgkin-Huxley or network conductance models).
* Advanced proficiency in MATLAB/Python programming for computational model development.
* Strong track record of independent research and peer-reviewed scientific publications.
Preferred Qualifications
* Background in neuroendocrinology, hypothalamic circuitry, or metabolic regulation (e.g., glucose homeostasis).
* Experience aligning complex computational models with variable clinical datasets, such as blood titration data or hormone profiles.
* Ability to work in a collaborative, multi-disciplinary team setting encompassing systems biology, genetics, and clinical data collection.
Postdoctoral Fellow #2) Computational Systems Biology
Institution: Morgan State University, Baltimore, MD, USA
Project: Digital twins for the therapeutics of gestational diabetes (NIH-funded: OTA-26-004)
Position Summary: We are seeking a Postdoctoral Fellow to lead the computational modeling of 1) metabolic systems and 2) fluid balance related to hypertension. As part of an innovative NIH-funded grant, this project focuses on constructing patient-specific digital twins to advance glycemic management and therapeutic strategies across diverse pregnant cohorts.
Required Qualifications
* Ph.D. in Applied Mathematics, Mathematical Biology, Systems Biology, or a related discipline.
* Strong background in mechanistic mathematical modeling of biological, endocrine, or physiological systems.
* Advanced proficiency in MATLAB/Python programming.
* Demonstrated record of scientific publication and strong writing skills.
Preferred Qualifications
* Prior experience with metabolic signaling, glucose-insulin models, or gastrointestinal dynamics.
* Familiarity with Python/PyTorch or cross-disciplinary clinical research environments.
Postdoctoral Fellow #3) Computational and statistical analysis of spatial biology
Institution: Morgan State University, Baltimore, MD, USA
Position Overview
We are seeking a highly motivated and innovative Postdoctoral Fellow to lead the computational and statistical analysis of high-dimensional multi-modal spatial data. The successful candidate will develop and apply advanced statistical models to integrate spatial transcriptomics, clinical neuroimaging, and multiplexed tissue imaging datasets. You will play a critical role in bridging the gap between macro-scale brain imaging and cellular-level spatial omics.
Key Responsibilities
* Data Analysis & Pipeline Development: Lead the statistical analysis of spatial transcriptomics data (e.g., 10x Visium, Xenium, or MERFISH) and highly multiplexed imaging data (e.g., CODEX).
* Multi-Modal Brain Mapping: Process and analyze neuroimaging datasets (e.g., structural MRI, fMRI, or PET) and integrate these macro-scale clinical images with micro-scale spatial multi-omics to build multiscale models of brain tissue.
* Algorithm & Method Development: Design and implement novel statistical and machine learning methods for spatial domain identification, image segmentation, and cross-modality data alignment.
* Collaboration: Work closely with experimentalists and radiologists to guide experimental design, ensure data quality, and iterate on analytical approaches.
* Scientific Communication: Prepare high-impact manuscripts, present findings at national/international conferences, and assist in drafting computational sections for grant proposals.
Required Qualifications
* Education: Ph.D. in Applied Mathematics, Mathematical Biology, Biostatistics, Bioinformatics, Neuroinformatics, Data Science, or a related quantitative field.
* Statistical Expertise: Strong foundation in statistical modeling, hypothesis testing, and spatial statistics.
* Domain Experience: Proven hands-on experience analyzing spatial transcriptomics datasets, quantitative imaging sciences, and/or neuroimaging data.
* Programming Skills: High proficiency in R and/or Python. Experience with relevant computational libraries (e.g., Seurat, Squidpy, SpatialExperiment).
* Version Control & Compute: Experience with Git/GitHub and working in high-performance computing (HPC) or cloud environments.
* Communication: Excellent written and oral communication skills, with a track record of peer-reviewed publications.
Preferred Qualifications (Optional)
* Proficiency with standard neuroimaging analysis software and pipelines (e.g., FSL, FreeSurfer, AFNI, SPM, or ANTs).
* Experience with deep learning and computer vision techniques applied to biological or medical images (e.g., medical image registration, cell segmentation).
* Strong background in neuroanatomy, neurobiology, or cognitive neuroscience.
Contact
Send me your CV to Pilhwa Lee, Pilhwa.lee@morgan.edu