Job Description

Job Title: H&E Image Analysis Scientist / Machine Learning Engineer- Spatial Omics (PhD) Experience: Freshers Location: Delhi Job Description: We are seeking a motivated PhD candidate interested in machine learning for histopathology image analysis. The candidate will contribute to developing and optimizing deep learning models to analyze digitized H&E slides for cancer classification and spatial mapping. This role is well-suited for researchers aiming to apply advanced computational methods to biomedical challenges. Responsibilities: ? Design, develop, and train convolutional neural networks (CNNs) and related ML models on H&E-stained histology images. ? Use and extend tools such as QuPath for cell annotations, segmentation models, and dataset curation. ? Preprocess, annotate, and manage large image datasets to support model training and validation. ? Collaborate with cross-disciplinary teams to integrate image-based predictions with molecular and clinical data. ? Analyze model performance and contribute to improving accuracy, efficiency, and robustness. ? Document research findings and contribute to publications in peer-reviewed journals. Qualifications: ? PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related discipline. ? Demonstrated research experience in machine learning, deep learning, or biomedical image analysis (e.g., publications, thesis projects, or conference presentations). ? Strong programming skills in Python and experience with ML frameworks such as TensorFlow or PyTorch. ? Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools. ? Ability to work collaboratively in a multidisciplinary research environment. Preferred: ? Background in cancer histopathology or biomedical image analysis. ? Knowledge of multimodal data integration, including spatial transcriptomics.