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Field
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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perturbation, live and fixed imaging, and mechanical measurement or perturbation as the questions require; Analysing, interpreting, and integrating the resulting data with the lab’s existing multiomic datasets
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repeatability Preparation of high-quality journal publications, conference outputs, project reports, and industry-focused documentation Contribution to prototype development, technology translation, and
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, reinforcement learning, simulation, and counterfactual analysis. Multimodal NLP & Fusion, large language models (LLMs), cross-attention Fusion, vision-language transformers. Ontological engineering, knowledge
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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: Education: PhD in Toxicology Work Experience: Technical background experience in molecular biology, cellular imaging and animal experimentation in liver diseases Experience in bioinformatics Required
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for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop the research capacity through the application of the selected methods; - exercise a critical
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mammary epithelial cell systems · Mouse and genetically engineered cancer models · High-content and live-cell imaging · Tumor evolution and therapeutic response modeling Research
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childhood cancer The lab has extensive expertise in: · Human primary mammary epithelial cell systems · Mouse and genetically engineered cancer models · High-content and live-cell imaging
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: · Human primary mammary epithelial cell systems · Mouse and genetically engineered cancer models · High-content and live-cell imaging · Tumor evolution and therapeutic response modeling