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Computational modeling and high-density microelectrode array analysis forne

Computational modeling and high-density microelectrode array analysis forne

The University of Texas MD Anderson Cancer CenterSacramento, CA, US
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Job Title : Computational modeling and high-density microelectrode array analysis forne

Job Number : 105308

Location : Sacramento, US

RESEARCH FOCUS

The Ben-Shalom laboratory investigates neurodevelopmental disorders (NDDs) using a multiscale approach integrating high-density microelectrode array (HD-MEA) recordings with biophysically detailed computational modeling. Our research focuses on understanding how genetic mutations alter neuronal excitability and network dynamics, leading to epilepsy and other NDDs. The candidate will develop and validate in silico neuronal models using empirical data from HD-MEA recordings to bridge single-neuron biophysics with circuit-level dysfunction and identify therapeutic targets.

RESPONSIBILITIES

  • Computational Modeling : Develop biophysically detailed neuronal models using NEURON, NetPyNE, and Python / MATLAB
  • HD-MEA Data Analysis : Process HD-MEA recordings including spike sorting and network characterization
  • Model Validation : Integrate experimental data to validate computational models
  • Network Simulations : Simulate cortical networks to predict mutation effects and identify therapeutic interventions
  • Data Integration : Combine multi-scale data with computational predictions using statistical and ML approaches
  • Research Dissemination : Prepare manuscripts and present at conferences

QUALIFICATIONS

  • Ph.D. in computational neuroscience, biophysics, bioengineering, or related field
  • Essential : Experience with computational neuronal modeling (NEURON, NetPyNE, or similar)
  • Highly Preferred : Experience with microelectrode array data analysis and spike sorting
  • Strong programming skills in Python, MATLAB, and / or C++
  • Familiarity with ion channel biophysics and Hodgkin-Huxley modeling
  • Experience with large-scale data analysis and statistical methods
  • Knowledge of machine learning approaches preferred
  • Excellent communication skills and ability to work in teams
  • RESEARCH ENVIRONMENT

  • Join a dynamic, collaborative laboratory at UC Davis with state-of-the-art HD-MEA recording systems, patch-clamp electrophysiology, and high-performance computing resources.
  • Access to cutting-edge HD-MEA technology
  • Collaboration with experimental neuroscientists, clinicians, and computational researchers
  • Training in advanced electrophysiological techniques and organoid culture
  • Professional development through conferences and grant writing
  • Mentoring opportunities with graduate students and postdocs
  • APPOINTMENT DETAILS

    Competitive salary commensurate with NIH postdoctoral scales and experience. Excellent career development opportunities with potential for renewal based on performance and funding. Review of applications begins immediately and continues until filled.

    Application Deadline : 2026-03-05

    J-18808-Ljbffr

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    Modeling And • Sacramento, CA, US

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