Beskrivning
Our client is building the world's first foundation model for physical infrastructure: electricity, gas, heat, and water.
The networks that power modern civilization have run for decades on 1970s control logic, under-observed, under-optimized, and increasingly unequal to the demands of the energy transition. Our client was founded to change that.
They have already built a composite, physics-grounded causal world model: six co-trained inference engines spanning physics-informed GNNs, causal time-series, topology discovery, federated training, edge inference, and techno-economic optimization. It is in production at 30 distribution system operators across 14 countries, covering 22 million connection points. Several of the world's largest industrial automation and grid-technology vendors integrate it into their platforms, and it runs on the live grids of multiple tier-1 European utilities today.
They are now building the next layer: a large-scale pre-trained foundation model for flow networks, trained on tens of billions of physics-consistent network states and governed by hard conservation-law constraints that no language model will ever learn from tokens. This is not applied AI. It is a new model class.
DeepRec.ai is partnering with the company to assemble a small, exceptional team to build it. The data is real, exclusive, and unglamorous. The physics is non-negotiable. The impact is continental.
The problem you will work onDistribution grids are among the most complex dynamical systems on Earth: millions of nodes, time-varying topology, hard physical constraints, and almost no labeled ground truth. The state of the art is classical SCADA with a thin ML veneer.
Our client is replacing it with a large-scale pre-trained foundation model, trained on synthetic and real network states, governed by Kirchhoff constraints as a hard loss term, and fine-tuned on operator-specific topologies via federated learning.
Stage 1 pre-training target: 10¹? Newton-Raphson power-flow solutions across 50,000 distribution topologies. Stage 2: cross-network generalization to gas, heat, and water flow networks. Same architecture, different conservation laws.
What you will do
The networks that power modern civilization have run for decades on 1970s control logic, under-observed, under-optimized, and increasingly unequal to the demands of the energy transition. Our client was founded to change that.
They have already built a composite, physics-grounded causal world model: six co-trained inference engines spanning physics-informed GNNs, causal time-series, topology discovery, federated training, edge inference, and techno-economic optimization. It is in production at 30 distribution system operators across 14 countries, covering 22 million connection points. Several of the world's largest industrial automation and grid-technology vendors integrate it into their platforms, and it runs on the live grids of multiple tier-1 European utilities today.
They are now building the next layer: a large-scale pre-trained foundation model for flow networks, trained on tens of billions of physics-consistent network states and governed by hard conservation-law constraints that no language model will ever learn from tokens. This is not applied AI. It is a new model class.
DeepRec.ai is partnering with the company to assemble a small, exceptional team to build it. The data is real, exclusive, and unglamorous. The physics is non-negotiable. The impact is continental.
The problem you will work onDistribution grids are among the most complex dynamical systems on Earth: millions of nodes, time-varying topology, hard physical constraints, and almost no labeled ground truth. The state of the art is classical SCADA with a thin ML veneer.
Our client is replacing it with a large-scale pre-trained foundation model, trained on synthetic and real network states, governed by Kirchhoff constraints as a hard loss term, and fine-tuned on operator-specific topologies via federated learning.
Stage 1 pre-training target: 10¹? Newton-Raphson power-flow solutions across 50,000 distribution topologies. Stage 2: cross-network generalization to gas, heat, and water flow networks. Same architecture, different conservation laws.
What you will do
- Own end-to-end pre-training of the physics-informed GNN foundation model: data pipeline design, masked pre-training objective, distributed training infrastructure, and evaluation harness.
- Characterize scaling laws for physics-informed pre-training: data efficiency vs. compute trade-offs, emergence of physical consistency, and OOD generalization across unseen topologies.
- Design the pre-training corpus: synthetic topology generation, power-flow simulation at scale, and augmentation strategies that preserve physical validity.
- Lead the foundation-model preprint: own the architecture and pre-training sections, targeting a top-tier venue (NeurIPS, ICLR, ICML) or arXiv first.
- Interface with the causal world-model team on physics-informed loss formulation, and with the federated training team on privacy-preserving pre-training across operator estates.
- Represent our client externally at frontier AI venues. We expect this person to be a recognizable scientific voice for the model class being defined.
- PhD in machine learning, computer science, or computational physics from a leading research institution (e.g. ETH Zurich, Cambridge, Oxford, TU Munich, EPFL, UCL, ENS, or equivalent).
- 3 to 6 years of post-PhD experience at a frontier AI lab or leading academic group (e.g. DeepMind, Meta FAIR, Mistral, EleutherAI, Stability AI, Kyutai, Aleph Alpha, Max Planck MIS, IDSIA, ELLIS-network member labs, or equivalent).
- First-author publications at NeurIPS, ICLR, or ICML on large-scale pre-training, masked modeling, GNN expressivity or scaling, or physics-informed deep learning.
- Hands-on experience training models at >1B parameter scale with distributed GPU/TPU infrastructure (PyTorch DDP/FSDP, JAX, or equivalent).
- Desirable: prior work at the intersection of graph neural networks and physical simulations, including molecular dynamics, fluid dynamics, power systems, or any PDE-governed network system.
- Desirable: experience with physics-informed neural networks (PINNs), neural operators (FNO, DeepONet), or Hamiltonian / Lagrangian networks.
- A genuinely unsolved research problem at the intersection of physics, ML, and critical infrastructure, with exclusive access to real production data from 30 grid operators.
- First-principles technical latitude: you define the pre-training objective, the architecture choices, and the evaluation methodology, subject to hard physical constraints, not product-manager preference.
- A small, senior team. You will work directly with world-leading researchers in physics-informed ML and graph-based power systems AI.
- Competitive compensation benchmarked to tier-1 European AI labs, with meaningful equity in a company with €4M committed capital and growing ARR.
- Publication and conference travel fully supported.
OM FÖRETAGET
DEEPREC.AI
