Postdoctoral Fellow: Computational Neuroscience

Society for Neuroscience; Baltimore (MD); On-site; USD 60,000 - 75,000; Full time

Job summary

Morgan State University in Baltimore is seeking a Postdoctoral Fellow to contribute to the computational modeling of hypothalamic neuronal excitability and metabolic adaptation as part of an NIH-funded digital twin project.

You will drive mechanistic models, integrate hormonal data, and advance understanding of glucose control and energy balance across healthy, obese, and diabetic maternal cohorts.

Qualifications

• Ph.D. in Neuroscience, Mathematical Biology, Applied Mathematics, 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.

Responsibilities

• Construct mechanistic models of hypothalamic neuronal circuitry to capture hormonal modulation of excitability.
• Integrate metabolic and placental hormone data to study glucose control and energy balance across different maternal states.
• Collaborate with multi-disciplinary teams to advance NIH-funded digital twin project and publish findings.

Skills

Computational modeling

Neuronal excitability

Independent research

MATLAB/Python

Education

Ph.D. in Neuroscience or related

Tools

MATLAB

Python

Job description

Postdoctoral Fellow - 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 Neuroscience, Mathematical Biology, Applied Mathematics, 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.