Student Ambassadors

We're looking for three students to help develop induction courses for new teaching staff about digital learning tools at QMUL. You'll work directly with the Technology Enhanced Learning (TEL) team to create training materials that help staff use technology effectively in their teaching. 

Main Responsibilities

1. Help create online self-paced training courses for new teaching staff 
2. Assist in developing in-person induction sessions for new teaching staff 
3. Participate in creating student case study videos 
4. Share your experience with digital learning tools at QMUL 
5.  Provide student perspective on how teaching staff can better use technology 

This is an exciting opportunity to gain:

• Professional experience in digital education 
• Content development skills 
• Project collaboration experience 
• Understanding of educational technology at Queen Mary 
• Opportunity to represent the student voice in the development of digital learning initiatives.
• The training provided can enhance your CV and develop transferable skills. 

Your input will help shape how new teaching staff learn to use technology in their classes. You'll represent student voices in improving digital education at QMUL while gaining valuable professional experience. 

Time Commitment: 10-15 hours per week for approx. 3 months

Currently enrolled at QMUL 

Essential

1. Currently enrolled student at QMUL 
2. Good knowledge of QMUL's digital learning platforms (QMplus, Q-Review and more.) 
3. Strong communication skills 
4.  Ability to explain technical concepts clearly 
5. Good teamwork skills 
6. Willingness to work collaboratively with other student ambassadors and the TEL Team. 
7. Ability to pick up new technologies in short order. 

Desirable

1. Video editing and production 
2. Media creation 
3. Presentation 

About the Role:

We are seeking a highly motivated Research Assistant to contribute to the development of a medical timeline builder using Large Language Models (LLMs). This project aims to extract and organize temporal information from clinical narratives to construct structured medical timelines that enhance clinical decision-making and patient care. The successful candidate will work at the intersection of natural language processing (NLP), clinical informatics, and AI-driven healthcare applications.

Key Responsibilities:

• Data Processing & Annotation: Preprocess and structure clinical text datasets (e.g., i2b2, MIMIC) for training and evaluation.
• LLM Fine-Tuning & Evaluation: Fine-tune state-of-the-art LLMs for temporal information extraction and reasoning in clinical texts.
• Pipeline Development: Develop and implement a two-stage LLM-based framework for extracting temporal references and constructing medical timelines.
• Model Benchmarking: Design benchmark datasets and evaluate models on clinical temporal reasoning tasks.
• Visualization & Integration: Assist in integrating timeline generation results into interactive visualization toolsfor clinical use.
• Collaboration & Dissemination: Work closely with interdisciplinary teams, including clinicians and AI researchers, and contribute to publications and conference presentations.

Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Biomedical Informatics, or a related field.

• Programming Skills: Proficiency in Python, with experience in NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK).
• Machine Learning & LLMs: Understanding of deep learning, LLM fine-tuning, and model evaluation techniques.
• Clinical NLP Experience: Familiarity with medical text processing, clinical terminologies (e.g., SNOMED, UMLS), and temporal reasoning in healthcare.
• Data Handling: Experience working with structured and unstructured clinical datasets (e.g., i2b2, MIMIC-III).
• Research & Communication: Strong analytical skills, ability to conduct literature reviews, and contribute to academic writing.

• assisting in conducting research activities related to computer vision, including literature reviews, data collection, experimentation, and analysis
• assisting in the development and implementation of computer vision algorithms, including image processing, object detection, recognition, segmentation, and tracking
• preparing and annotating datasets for training and evaluation purposes, ensuring data quality and relevance to research objectives
contributing to the solution in a form of software tools and frameworks for computer vision research, using programming languages such as Python or C/C++
• assisting in the analysis of qualitative and quantitative data, as directed.

N/a

• Some prior experience and strong interest in the subject of Computer Vision
• Understanding of deep learning frameworks (e.g., TensorFlow Keras, PyTorch) and some proficiency in training convolutional neural networks (CNNs) for computer vision tasks.
• Familiarity in training deep learning models using preprocessed and augmented datasets, monitoring model performance and convergence during training.
• Practical knowledge in utilizing programming languages relevant to machine learning, deep learning and computer vision (Python 3.4 and above is an absolute must).
• Experience working with video / image data, including data preprocessing, annotation and analysis using popular libraries (e.g. OpenCV)
Knowledge of common evaluation metrics for assessing model performance in computer vision tasks, such as accuracy, precision, recall, and F1 score.
•  Knowledge in web frameworks written in Python (e.g. Flask) is desirable but not essential