Neural Data Scientist

Max Planck Florida; Florida; On-site; USD 120,000 - 180,000; Full time

Job summary

Max Planck Florida Institute for Neuroscience seeks a Neural Data Scientist to advance a Neuro-AI initiative focused on AI-driven analysis and predictive modeling. The role collaborates with experimental groups to translate questions into precise hypotheses and to design experiments through theory-driven approaches.

You will develop deep learning and dynamical modeling frameworks for large multi-modal neural datasets, including imaging, electrophysiology, and behavior, and build GUI-based

Qualifications

• PhD in ML, CS, Biomedical Engineering, Computational Neuroscience or related field.
• Proven expertise in AI/ML, especially deep learning, applied to neural or biosensor data.
• Experience collaborating with experimentalists, contributing to experimental design or hypothesis refinement.
• Proficiency in Python and ML frameworks including PyTorch, TensorFlow, and JAX.

Responsibilities

• Translate biological questions into precise, testable hypotheses about neural dynamics, computation, and behavior.
• Support experimental design by applying theory.
• Develop deep learning and dynamical modeling frameworks for multi-modal neural data (imaging, electrophysiology, behavior).
• Build shared computational tools and GUI-based analysis pipelines for researchers.

Skills

AI/ML

Python

PyTorch

TensorFlow

JAX

Deep learning

Education

PhD

Tools

HPC

Cloud computing

Job description

Max Planck Florida Institute for Neuroscience is seeking a Neural Data Scientist to support a funded Neuro-AI initiative focused on AI-driven analysis and predictive modeling.

Responsibilities

• Collaborate with MPFI experimental research groups to convert open biological questions into precise, testable hypotheses about neural dynamics, computation, and behavior
• Support experimental design by applying theory
• Develop deep learning and dynamical modeling frameworks for large-scale, multi-modal neural datasets, including:
  • In vivo functional imaging
  • High-density electrophysiology
  • Behavior measurements
  • Optical or biosensor-derived signals
• Build shared computational tools and platforms, including accessible, GUI-based analysis pipelines, enabling experimentalists to engage with modeling results and refine hypotheses together
• Oversee scientific use of central and/or cloud-based HPC resources for computationally intensive modeling, large-scale data analysis, model training, and reproducible scientific workflows for Neuro-AI projects

Requirements

• PhD in Machine Learning, Computer Science, Biomedical Engineering, Computational Neuroscience, Biophysics, or a related quantitative/biological field
• Proven expertise in AI/ML, especially deep learning, applied to biological, neural, or biosensor datasets
• Documented experience collaborating closely with experimentalists, ideally contributing to experimental design or hypothesis refinement (not limited to post-hoc analysis)
• Proficiency in Python and common ML frameworks, including PyTorch, TensorFlow, and JAX
• Strong record of scientific publications and collaborative, cross-disciplinary research

Technologies

• Python
• PyTorch
• TensorFlow
• JAX

Preferred

• 2+ years of postdoctoral or equivalent research experience
• Background in systems neuroscience, neural recording techniques (electrophysiology and functional imaging such as multiphoton and single-photon calcium and/or neurotransmitter imaging), or biosensor technologies
• Experience developing robust, validated scientific software tools, analysis platforms, or data pipelines for multidisciplinary research teams
• Experience deploying reproducible analysis of workflows, machine learning models, or scientific software on HPC clusters and/or cloud-based computing environments
• Familiarity with dynamical systems, control theory, or generative/probabilistic modeling approaches to behavior and neural data

Location: Jupiter, FL (onsite)

Education: PhD