AI Research Scientist - Machine Learning for Behavioral AI
Tasks
- Benchmark models against state of the art methods
- Build foundation models for generative and predictive learning
- Create scalable pretraining pipelines
- Curate and preprocess molecular and crystal datasets
- Deploy generative models for property conditioned design
- Design reinforcement learning strategies for synthesizable candidates
- Develop generative models for molecular design
- Embed generative design into LLM based multi agent systems
- Evaluate validity novelty uniqueness stability synthesizability
- Integrate first principles calculations into generative workflows
- Integrate physics informed reward signals
- Interface generative design modules with robotic workcells and characterization instruments
- Plan autonomous experiments using candidate generation
- Predict crystal structures using crystal structure prediction
- Run molecular dynamics simulations for candidate validation
- Train generative models for crystal structure generation
Perks/Benefits
- N/A
Skills/Tech-stack
3D conformers | ASE | Agent systems | Alignment | Autoregressive models | Computational Chemistry | Continuous Normalizing Flows | Crystal Structure Prediction | Crystallography | Deep learning | Density Functional Theory | Diffusion Models | Equivariant Neural Networks | Flow matching | Force Fields | Foundation Models | Gaussian | Generative Modeling | Geometric Deep Learning | Graph Neural Networks | Interatomic potentials | JAX | LAMMPS | Language Models | Large Language Models | Lattice Energy Ranking | Machine Learned Interatomic Potentials | Molecular Dynamics | Molecular graphs | Multi-Agent | Multi-Agent Systems | Neural Networks | Normalizing flows | OpenMM | Physics-informed learning | PyTorch | Python | RLHF | Reinforcement Learning | SMILES | Structure prediction | Transformer | VASP | Variational Autoencoders
Education
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