Machine learning Engineer
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A highly motivated Machine Learning Engineer specialising in drug discovery. Skilled in developing predictive models, optimising drug efficacy and safety through multi-objective algorithms, and managing pharmaceutical data to identify novel compounds and therapeutic targets. Experienced in using advanced computational techniques such as QSAR modelling, molecular docking simulations, and neural network architectures to streamline drug development processes.
Demonstrates strong analytical and problem-solving abilities, with a focus on leveraging data-driven approaches to accelerate innovation in pharmaceutical research
A highly motivated Machine Learning Engineer specialising in drug discovery. Skilled in developing predictive models, optimising drug efficacy and safety through multi-objective algorithms, and managing pharmaceutical data to identify novel compounds and therapeutic targets. Experienced in using advanced computational techniques such as QSAR modelling, molecular docking simulations, and neural network architectures to streamline drug development processes.
Demonstrates strong analytical and problem-solving abilities, with a focus on leveraging data-driven approaches to accelerate innovation in pharmaceutical research.
i. Conducted a comparative analysis combining SMOTE and undersampling methods with various supervised learning algorithms to determine which approach best addresses class imbalance in pharmaceutical datasets, focusing on applications in drug discovery.
ii. PHD Proposal “Novel multi-objective optimisation methods for multi-label classification in drug repurposing” : Implemented multi-objective optimisation techniques to balance drug efficacy and toxicity in molecular design.
i. Biochemistry modules ii. Conducted a comparative study of feature selection methods to improve the accuracy of QSAR (Quantitative Structure-Activity Relationship) models.
