k-dense-ai

1★

Run molecular machine learning workflows with DeepChem for property prediction, featurization, and benchmarked datasets.

1★

Build Bayesian models with PyMC using hierarchical models, MCMC, variational inference, and posterior checks.

1★

Orchestrate scalable PyTorch training with Lightning modules, trainers, callbacks, and distributed execution.

1★

Build, simulate, transpile, and execute quantum circuits with Qiskit and IBM Quantum Runtime.

1★

Perform cheminformatics analysis with RDKit for molecular parsing, descriptors, fingerprints, reactions, and similarity.

1★

Run standard single-cell RNA-seq analysis with Scanpy for QC, clustering, visualization, and differential expression.

1★

Model single-cell omics data with scvi-tools for batch correction, transfer learning, and multimodal integration.

1★

Train reinforcement learning agents with Stable-Baselines3 using PPO, SAC, DQN, TD3, and related algorithms.

1★

Build graph neural network workflows with PyTorch Geometric for node, link, and graph prediction.

1★

Work with Hugging Face Transformers for model loading, pipeline inference, generation, and trainer fine-tuning.