Project Overview: We are seeking an experienced Machine Learning Specialist to assist in the development and implementation of machine learning models for the controlled synthesis of carbon dots (CDs). This project involves data-driven prediction of optical properties of CDs based on key reaction parameters. The ideal candidate will have expertise in machine learning algorithms, data preprocessing, feature engineering, and model optimization. Knowledge of Python and relevant libraries (e.g., Pandas, Scikit-learn) is essential. Project Description: The project aims to apply machine learning techniques to predict and optimize the synthesis of carbon dots, focusing on properties like fluorescence intensity, emission wavelength, and stokes shift. The dataset comprises experimental data on 80 synthesis conditions, with key parameters such as precursor types, solvent types, and reaction time. The successful candidate will need to: Preprocess and clean data. Apply dimensionality reduction techniques like PCA for feature engineering. Compare different machine learning models (XGBoost, Random Forest, Ridge Regression, etc.) to identify the most effective model. Evaluate and optimize the selected model for accurate predictions. Visualize model performance using metrics like Mean Absolute Error (MAE) and R². Verify the model's generalization ability with new experimental data. Key Responsibilities: Dataset Construction: Collect and structure experimental data, including reaction parameters and target properties for carbon dot synthesis. Data Preprocessing: Handle complex solvent data and apply transformation methods such as PCA and standardization to prepare the dataset for training. Feature Engineering: Use domain knowledge to generate meaningful features from raw data, ensuring they contribute to model accuracy. Model Development: Implement and train various machine learning models (XGBoost, Random Forest, Ridge, LGBM, etc.). Model Evaluation: Use performance metrics (MAE, R²) to assess model accuracy and optimization. Visualization and Reporting: Create heatmaps and graphs to visualize correlations, model performance, and feature importance. Model Optimization: Fine-tune hyperparameters to improve model generalization and accuracy. Skills and Requirements: Proven experience in machine learning, especially in supervised learning models. Expertise in Python programming and libraries such as Pandas, Scikit-learn, XGBoost, and matplotlib. Solid understanding of data preprocessing techniques (e.g., PCA, standardization, feature engineering). Familiarity with machine learning evaluation metrics like R², MAE, RMSE, and Pearson correlation. Experience in visualizing data and model results (heatmaps, decision trees, etc.). Ability to write clean, well-documented code and provide clear explanations of model outcomes. Knowledge of chemistry or materials science is a plus but not mandatory. Project Deliverables: Preprocessed and cleaned dataset. Trained and optimized machine learning model with performance metrics. Visualizations of feature importance and model performance. A detailed report explaining model choices, evaluation metrics, and predictions. Timeline: The project is expected to last approximately 3–4 weeks, depending on the availability and the complexity of model tuning.