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Medical Physics Internship Topics

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Medical Physics Internships with Accommodation

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Showing 1–12 of 100 internship topics
Deep Learning Models for Dose Distribution Prediction
Interns will develop and train convolutional neural networks to predict 3D dose distributions in radiation therapy treatment planning. They will work with clinical CT imaging data, validate predictions against TPS algorithms, and optimize model architectures for accuracy and computational efficiency.
AI Radiation Therapy Optimization ResearchView internship →
Treatment Plan Optimization Using Reinforcement Learning
Interns will implement reinforcement learning algorithms to optimize beam angles, weights, and intensity patterns for improved tumor coverage and organ sparing. They will develop reward functions aligned with clinical objectives and benchmark results against conventional optimization techniques.
AI Radiation Therapy Optimization ResearchView internship →
Automated Organ Segmentation and Contour Refinement
Interns will create and refine machine learning models for automated segmentation of organs at risk and target volumes from medical imaging. They will evaluate segmentation accuracy, develop semi-automated contour correction tools, and assess clinical applicability for treatment planning acceleration.
AI Radiation Therapy Optimization ResearchView internship →
Patient-Specific QA Prediction Using Machine Learning
Interns will build predictive models to identify high-risk treatment plans and predict potential quality assurance failures before delivery. They will analyze historical patient data, develop early warning algorithms, and create decision support tools for clinical implementation.
AI Radiation Therapy Optimization ResearchView internship →
Multi-Objective Optimization for Adaptive Radiation Therapy
Interns will research and implement multi-objective optimization algorithms that balance competing clinical goals in adaptive radiation therapy workflows. They will develop methods to handle plan re-optimization based on anatomical changes and create tools to support rapid clinical decision-making during treatment courses.
AI Radiation Therapy Optimization ResearchView internship →
Deep Learning for CT Image Reconstruction
Interns will develop and train convolutional neural networks to improve CT image reconstruction from sparse or noisy projection data. They will work with real clinical datasets to implement physics-informed learning approaches that reduce radiation dose while maintaining diagnostic image quality.
Machine Learning Medical Imaging Physics ResearchView internship →
Radiomics Feature Extraction and Tumor Classification
Interns will extract quantitative imaging biomarkers from medical images and develop machine learning models for automated tumor classification and prognosis prediction. This involves implementing feature selection algorithms, model validation, and clinical correlation studies.
Machine Learning Medical Imaging Physics ResearchView internship →
Adversarial Robustness in Medical Image Segmentation
Interns will investigate the vulnerability of segmentation networks to adversarial attacks and develop robust deep learning models for organ and lesion segmentation in medical images. They will implement defensive techniques and test models against adversarial perturbations relevant to clinical imaging scenarios.
Machine Learning Medical Imaging Physics ResearchView internship →
Generative Models for Medical Image Synthesis and Enhancement
Interns will design and train generative adversarial networks (GANs) and diffusion models to synthesize realistic medical images, perform image-to-image translation, and enhance low-resolution or artifact-laden images. Applications include cross-modality synthesis between CT, MRI, and PET imaging.
Machine Learning Medical Imaging Physics ResearchView internship →
Federated Learning for Multi-Institutional Medical Imaging Analysis
Interns will develop federated learning frameworks that enable collaborative model training across multiple medical institutions while preserving patient privacy. They will implement distributed machine learning algorithms for diagnostic tasks and evaluate performance on decentralized clinical datasets.
Machine Learning Medical Imaging Physics ResearchView internship →
Deep Learning for MRI Image Reconstruction
Interns will develop and train neural networks to reconstruct high-quality MRI images from undersampled k-space data. They will work with convolutional neural networks and recurrent architectures to improve reconstruction speed and image quality while reducing artifact generation.
AI MRI Physics Development ResearchView internship →
Quantitative MRI Parameter Estimation
Interns will research AI algorithms for extracting quantitative tissue parameters (T1, T2, diffusion coefficients) from multi-echo and multi-flip angle MRI sequences. This involves developing machine learning models to accelerate acquisition protocols while maintaining diagnostic accuracy.
AI MRI Physics Development ResearchView internship →
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