6 of 6 Permanent Random Forest Jobs in England

such as GraphRAG and agentic RAG to reduce hallucination and improve factual grounding. Technical Proficiency Machine learning algorithms: linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras Cloud computing platforms: AWS, Azure, GCP Natural language processing ...

optimization algorithms for business applications Preferred Qualifications Experience in professional software development Experience in standard machine-learning and statistical modeling tools and techniques (e.g. random forests, gradient-boosted regression, LASSO, logistic regression) Strong track record of publications at top-tier journals or conferences Experience with integer programming, dynamic programming ...

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 … debugging ML models across the modern Python stack - deep learning (CNNs, U-Nets, vision transformers) using PyTorch, as well as 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. ...

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 … debugging ML models across the modern Python stack - deep learning (CNNs, U-Nets, vision transformers) using PyTorch, as well as 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. ...

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 … debugging ML models across the modern Python stack - deep learning (CNNs, U-Nets, vision transformers) using PyTorch, as well as 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. ...

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 … debugging ML models across the modern Python stack - deep learning (CNNs, U-Nets, vision transformers) using PyTorch, as well as 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. ...