AI Research Scientist- Multimodal Foundational Models

Architect, design and validate multi-modal deep learning and Timeseries Foundation Models (TSFM) to work with multivariate time series signals.

Integrate and extend timeseries models to leverage information from other auxiliary modalities such as videos and images to enhance context understanding.

Offer expert insights to the management team in relevant technology sectors, aiding in strategic planning, R&D trajectory, and investment decisions.

Stay abreast of the latest technological innovations, document and disseminate research findings through high-caliber publications and/or patent submissions.

Expertise with Time Series FMs (beyond the time series task of forecasting).

In-depth experience in signal processing for sensor data and their integration with deep-learning methods.

Proficiency in Python, PyTorch (including libraries such as torchaudio, torchvision, torchmetrics), familiarity with PyTorch Lightning.

A strong publication record in relevant venues such as ICASSP, NeurIPS, InterSpeech, ICML, ICLR, KDD, ICRA, CVPR, ICCV…