Neural Data Scientist

**Science Operations**

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The Max Planck Florida Institute for Neuroscience (MPFI) is seeking a **Neural Data Scientist** to support a funded Neuro\-AI Initiative expanding the Institute's capacity for scientific computing, predictive modeling, and data analytics. This position provides a rare opportunity to work as an embedded computational neuroscientist within a world\-class experimental community.

The selected candidate will make a major contribution to the Institute’s groundbreaking discoveries and rapid advancement of neuroscience, applying their scientific expertise in AI and machine learning to the development of novel analytical tools that increase the pace of science, foster innovation and collaboration, and embed data science training throughout the scientific community.

The selected candidate will work closely with MPFI’s scientific leadership team to develop AI\-driven analysis and predictive modelling that deliver new insights into neural dynamics, brain states, and ultimately the cause of \- and solution to \- disorders of the brain.

The long\-term vision is to develop sustainable and validated, modular scientific computing platforms that can be shared with external collaborators and the broader neuroscience community through open science.

In this role, the selected candidate will:

• Partner directly with MPFI's experimental research groups to translate open biological questions into precise, testable hypotheses about neural dynamics, computation, and behavior
• Guide experimental design with theory.
• Develop deep learning and dynamical modeling frameworks for large\-scale, multi\-modal neural data — including *in vivo* functional imaging, high\-density electrophysiology, behavior, and optical or biosensor\-derived signals.
• Build shared computational tools and platforms — including accessible, GUI\-based analysis pipelines — that let experimentalists engage directly with modeling results and refine hypotheses collaboratively.
• Oversee the scientific utilization of central and/or cloud\-based high\-performance computing resources for computationally intensive modeling, large scale data analysis, model training, and reproducible scientific workflows supporting Neuro\-AI projects.

Cultivate a Neuro\-AI culture at MPFI: mentoring experimentalists in best practices and fostering a shared language between theory and experiment across labs.

*Required:*

• PhD in Machine Learning, Computer Science, Biomedical Engineering, Computational Neuroscience, Biophysics, or a related quantitative/biological field.
• Demonstrated expertise in AI/ML (especially deep learning) applied to biological, neural, or biosensor data sets.
• Track record of close collaboration with experimentalists — ideally including work that directly informed experimental design or hypothesis refinement, not just post\-hoc analysis.
• Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, JAX).
• A strong record of scientific publications and collaborative, cross\-disciplinary research.

*Preferred:*

• 2\+ years of postdoctoral or equivalent research experience.
• Background in systems neuroscience, neural recording techniques including electrophysiology and functional imaging (e.g. multiphoton and single\-photon calcium and/or neurotransmitter imaging), or biosensor technologies.
• Experience building robust and validated scientific software tools, analysis platforms, or data pipelines used by 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.

The selected candidate must be confident navigating a research\-oriented, fast\-paced scientific culture, comfortable moving fluidly between theory and the bench, and driven by genuine curiosity about the biology. Strong communication skills — including the ability to translate modeling insights for experimentalists and vice versa — are essential, along with sound judgment, discretion, and professional integrity.