Dear Comp Neuro community,
We are looking for a highly motivated
NeuroAI Research Scientist / Machine Learning Engineer to join a collaborative research effort between the
University of Rome Tor Vergata and
Tether.io
The position sits at the intersection of computational neuroscience, machine learning, foundation models, and brain-computer interfaces. The work will focus on developing open, reproducible methods for encoding and decoding brain activity
from invasive and non-invasive neural recordings, including fMRI, EEG/MEG, ECoG, iEEG, and related data modalities.
The successful candidate will contribute to projects involving neural decoding and encoding, multimodal representation learning, generative reconstruction of images, music, speech and other perceptual content, cross-subject and cross-modality alignment,
and the study of how brain activity maps onto the latent spaces of modern vision, language, and audio foundation models.
We are particularly interested in candidates with a background in machine learning, computational neuroscience, neuroimaging, signal processing, physics, computer science, engineering, or related quantitative fields. Strong Python and deep-learning experience,
especially with PyTorch, JAX, or TensorFlow, is expected. Experience with real neural data and/or modern generative and representation-learning models is highly valued.
The role offers the opportunity to contribute to high-impact publications, open-source tools, international collaborations, and ongoing research at the interface of AI, neuroscience, and neural interface technologies. Remote-friendly arrangements may be
possible depending on the role and project needs.
The LinkedIn announcement is available here:
To apply, please send a CV, a brief statement of research interests, and optionally 1–2 representative publications, projects, or GitHub links to:
Best regards,
Nicola Toschi