Deep Learning Research Intern

Pabel is an AI research lab building foundation models of functional brain biology. We're building a future where AI helps develop more effective therapies and brings precision medicine to neurology. Our partners are the NVIDIA Inception Program, Google for Startups, and the Neurology department of LMU Klinikum Großhadern (Prof. Dr. Jan Rémi). Tasks Conduct independent research on frontier foundation models for EEG and functional brain biology. Design, implement, and evaluate novel deep learning architectures and self-supervised learning methods in PyTorch. Investigate representation learning, biomarker transferability, generalization, scaling behavior, and model architectures for neurophysiological data. Read, reproduce, and extend the latest AI, machine learning, and computational neuroscience research. Write high-quality technical reports and research papers for Pabel's proprietary research library. Collaborate with AI researchers, neuroscientists, and clinical partners to translate scientific ideas into scalable AI systems. Requirements Currently pursuing a Bachelor's or Master's degree in Artificial Intelligence, Machine Learning, Computer Science, Mathematics, or a related field. Strong knowledge of deep learning and modern neural network architectures. Excellent Python and PyTorch skills. Able to think from first principles rather than relying on existing solutions. Comfortable reading, understanding, and implementing state-of-the-art machine learning research. Experience with self-supervised learning, foundation models, time-series modeling, neuroscience, or EEG is a plus. Passionate about building frontier AI that advances our understanding of the human brain. Benefits €20/hour (remote or on-site) Generous AI coding tool packages and GPU allocation for independent research Co-authorship opportunities on our published papers No bureaucracy, no review committees Potential for full-time conversion based on performance As a Research Intern , you will contribute to building the computational infrastructure that powers our breakthrough EEG foundation model research. You'll work at the intersection of neuroscience and machine learning, developing and optimizing pipelines that process massive EEG datasets and implementing cutting-edge deep learning experiments. This role offers hands-on experience with state-of-the-art neural decoding technology while working alongside world-class researchers pushing the boundaries of what's possible in EEG models.