3 of 3 Permanent Random Forest Jobs in Scotland

Jobs) and workflow orchestration with Apache Airflow. Strong working knowledge of both supervised and unsupervised techniques, including tree-based and kernel methods (gradient boosting, random forest, SVM) and anomaly-detection approaches such as Isolation Forest, Local Outlier Factor, and locality-sensitive hashing. Solid grounding in data ...

Jobs) and workflow orchestration with Apache Airflow. Strong working knowledge of both supervised and unsupervised techniques, including tree-based and kernel methods (gradient boosting, random forest, SVM) and anomaly-detection approaches such as Isolation Forest, Local Outlier Factor, and locality-sensitive hashing. Solid grounding in data ...

plantation mapping by region – palm oil, cocoa, coffee, rubber, soy, timber and similar – in support of EUDR compliance and supply‐chain due diligence. Forest degradation and biomass/canopy‐height estimation from multi‐sensor fusion. Develop ARR feasibility models that fuse climate, soil, and remote‐sensing inputs to estimate … evaluating, and debugging ML models across the modern Python stack – deep learning (CNNs, U‐Nets, vision transformers) using PyTorch, and classical methods (gradient boosting, random forests) with scikit‐learn. Demonstrable experience with remote sensing data (optical, SAR) and an understanding of the sensor‐specific quirks that matter for modelling. ...