Multimodal Jobs

I have safety sector time-series dataset that combines three synchronized streams: sensor imagery, textual maintenance logs, and high-frequency numeric readings. The objective is to forecast future values—not merely detect anomalies—so grid operators can anticipate demand, equipment stress, and renewable supply fluctuations. Because this is a research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clea...

I have safety sector time-series dataset that combines three synchronized streams: sensor imagery, textual maintenance logs, and high-frequency numeric readings. The objective is to forecast future values—not merely detect anomalies—so grid operators can anticipate demand, equipment stress, and renewable supply fluctuations. Because this is a research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clea...