“machine learning”

First-principles ML: classical methods, optimization, Gaussian processes, deep learning, probabilistic inference, generative models, RL.

**[REQUIRED]** For **ALL** data science and machine learning tasks.

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries.

Evaluate trained machine learning models with the right metrics and comparison logic.

Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models.

Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization.

Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines.

Machine learning in Python with scikit-learn.

Supports machine learning in Python with scikit-learn.

Fine-tune MACE machine learning interatomic potentials on custom datasets.

Fine-tune MatGL machine learning interatomic potentials on custom datasets.

Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.

Benchmark of inference speed of Machine Learning Interatomic Potentials (MLIPs).

Explain and audit machine-learning predictions with SHAP.

Train a property predictor head on top of a Machine Learning Interatomic Potential (MLIP) backbone (MACE or MatGL) to predict custom intensive or extensive…

Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes.

Supports geospatial research workflows for remote sensing, vector and raster GIS, spatial statistics, terrain and network analysis, and machine learning for…

Deploys anomaly detection for OT/ICS environments using machine learning on OT network baselines, physics-based process models, and Modbus/DNP3/OPC UA traffic…

Data science across machine learning, statistical modeling, and experimentation.

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential…

Automatiza a criação de frameworks de Machine Learning (Perceptron/Softmax) para Geociências com rigor acadêmico Qualis A1.

Use DeepXDE for scientific machine learning, PINNs, DeepONet/operator learning, backend selection, training, and troubleshooting.

Use DeepChem for molecular machine learning, data loading, featurization, model training, MoleculeNet, docking, and optional backend troubleshooting.

High-performance numerical computing and machine learning workflows using JAX.