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Medical Physics Internship Topics

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Medical Physics Internships with Accommodation

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Showing 25–36 of 100 internship topics
Image Reconstruction and Artifact Reduction in Nuclear Medicine
Interns will work on advanced image reconstruction algorithms using deep learning techniques like convolutional neural networks to enhance image quality while reducing radiation dose in SPECT and PET imaging. They will compare novel AI-based reconstruction methods against traditional iterative approaches.
AI Nuclear Medicine Physics ResearchView internship →
Deep Learning for Ultrasound Image Segmentation
Interns will develop and train convolutional neural networks to segment anatomical structures in ultrasound images. Work involves dataset preparation, model architecture design, and validation against clinical gold standards to improve automated organ and lesion detection.
AI Ultrasound Physics ResearchView internship →
Physics-Informed Neural Networks for Acoustic Wave Propagation
Interns will implement physics-informed neural networks (PINNs) to model ultrasound wave behavior in heterogeneous tissue. This research bridges computational physics and machine learning to improve simulation accuracy for treatment planning and image reconstruction.
AI Ultrasound Physics ResearchView internship →
Artifact Reduction and Image Quality Enhancement in Ultrasound
Interns will investigate AI-based methods to identify and reduce common ultrasound artifacts such as shadowing, reverberation, and speckle noise. Projects will involve developing preprocessing algorithms and evaluating their impact on diagnostic accuracy.
AI Ultrasound Physics ResearchView internship →
Real-Time Strain Elastography Analysis Using Machine Learning
Interns will apply machine learning techniques to extract tissue elasticity information from ultrasound strain data for non-invasive tissue characterization. Work includes algorithm development for motion tracking and classification of tissue stiffness patterns.
AI Ultrasound Physics ResearchView internship →
3D Volumetric Ultrasound Reconstruction and Registration
Interns will develop computational methods for reconstructing 3D volumes from 2D ultrasound sweeps and registering multi-temporal datasets. This involves spatial interpolation algorithms, feature matching, and validation using phantom and clinical data.
AI Ultrasound Physics ResearchView internship →
Deep Learning Models for Dose Distribution Prediction
Interns will develop and train convolutional neural networks to predict 3D dose distributions in radiotherapy treatment planning. They will work with clinical imaging data, validate model accuracy against Monte Carlo simulations, and optimize neural network architectures for real-time dose calculation in clinical settings.
AI Dosimetry ResearchView internship →
Automated Organ-at-Risk Segmentation Using Computer Vision
Interns will implement and refine AI algorithms for automatic segmentation of critical anatomical structures in CT and MRI images for dosimetric analysis. This involves dataset annotation, model training, performance evaluation against manual segmentations, and integration into treatment planning workflows.
AI Dosimetry ResearchView internship →
Dose Calculation Optimization with Machine Learning
Interns will investigate machine learning approaches to accelerate photon and electron dose calculations while maintaining clinical accuracy. They will compare algorithm performance, develop hybrid classical-AI methods, and validate results against established dose calculation engines used in radiotherapy clinics.
AI Dosimetry ResearchView internship →
AI-Driven Treatment Plan Quality Assurance and Prediction
Interns will create predictive models to automatically evaluate treatment plan quality, identify suboptimal dose distributions, and suggest plan improvements before clinical delivery. They will use historical clinical data to train classifiers and regression models for plan optimization recommendations.
AI Dosimetry ResearchView internship →
Uncertainty Quantification in AI-Based Dosimetry Systems
Interns will research methods to quantify and characterize uncertainties in machine learning-based dose prediction and optimization algorithms. They will implement Bayesian neural networks, uncertainty propagation analysis, and develop confidence metrics essential for clinical decision-making in radiotherapy.
AI Dosimetry ResearchView internship →
Monte Carlo Dose Calculation Algorithm Optimization
Interns will develop and optimize Monte Carlo algorithms for accurate radiation dose calculations in treatment planning systems. Work involves implementing variance reduction techniques, parallelization strategies, and validating computational efficiency against experimental benchmarks in clinical scenarios.
AI Monte Carlo Medical Simulation ResearchView internship →
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