Machine Learning and Neuro-Artificial Intelligence Specialist

Department:
Neurosurgery - ARIA Project

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Overview

The Department of Neurosurgery is seeking an experienced AI/ML professional and a knowledgeable AI enthusiast to help the ARIA team develop, use, and evaluate AI and apply it to deliver breakthrough research and applications at an unprecedented speed and scale. Guides projects within the research core, supporting core technologies, managing experimental design and data analysis. The Yale School of Medicine Department of Neurosurgery is seeking an AI researcher to develop machine-learning methods for large‑scale human brain data within the Aligning Research to Impact Autism (ARIA) program.

The work will focus on neural decoding, forecasting, representation learning, and multimodal modeling using intracranial EEG (iEEG/ECoG), scalp EEG, fNIRS, fMRI, and related data. The successful candidate will be an individual who enjoys working both independently and cooperatively with others, from a broad set of disciplinary backgrounds, on multiple projects involving a wide variety of topics and analytical tools.

Job Duties:

• Develop, train, and evaluate machine‑learning models for multimodal human brain data.
• Build models for neural representation learning, forecasting, decoding, and cross‑modal analysis.
• Contribute to the development of scalable brain foundation models across subjects, recording modalities, and tasks.
• Work with large datasets and optimize training pipelines for GPU and HPC environments.
• Collaborate with clinicians, neuroscientists, engineers, and other researchers on study design and analysis.
• Maintain clear, reproducible code and documentation for model development and evaluation.
• Contribute to manuscripts, conference presentations, and grant‑related research activities.
• Provide technical guidance to students and research staff when appropriate.

Required

Skills and Abilities

• Proven experience developing machine‑learning or deep‑learning models. Proven experience with distributed or multi‑GPU training and memory‑efficient model training.
• Familiarity with tools such as Slurm, CUDA, Docker or Apptainer, Conda, and scientific computing clusters.
• Strong Python programming skills and practical experience with PyTorch or a comparable deep‑learning framework.
• Proven experience with modern neural‑network architectures such as Transformers, self‑supervised learning, representation learning, generative models, or foundation models.
• Proven experience training models on GPUs and working in Linux and high‑performance computing environments.

Preferred

Skills and Abilities

• Proven experience with neural or physiological data, including iEEG/ECoG, EEG, fNIRS, fMRI, MEG, or related biosignals.
• Proven experience with time‑series modeling, neural forecasting, multimodal learning, or brain‑computer interface research.
• Proven experience with efficient attention or large‑model training methods, including scaled‑dot‑product attention, Flash Attention, mixed precision, or activation checkpointing.
• A research record in machine learning, computer vision, natural language processing, computational neuroscience, biomedical AI, or a related area.
• Proven experience developing machine‑learning or deep‑learning models for research applications.

• User Training:
  Provide advanced training and guidance to users of the research core.
• Experiment Support:
  Collaborate with…