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Student Ambassador Technology Enhanced Learning

£27,280 - £27,280
 

Job Description

For current students only.

Situated within IT Services, the Technology Enhanced Learning Team (TELT) provides strategic oversight of e-learning at QM and works to promote, support, and develop technology-enhanced learning across the institution. It is responsible for institution-wide learning applications such as:

  1. QMplus, the online learning environment (based on Moodle and Mahara)
  2. Q-Review lecture capture (based on Echo360)
  3. Turnitin, to assist with similarity detection
  4. Kaltura for media streaming

About the post

Main duties:

To deliver engaging and informative TELT inductions to new students during QM?s Welcome Week, 2024/25.

We are seeking an enthusiastic, confident, and tech-savvy student to join our team as a TEL (Technology Enhanced Learning) Student Ambassadors, to work with members of the TEL Team delivering TEL student inductions during Welcome Week, September 2024.

Last year we worked with 3 TEL Student Ambassadors to update and develop the TEL student induction programme, based on the feedback gathered from students. Our new improved inductions were well received, and we intend to deliver a similar programme in September 2024.

As a TEL Student Ambassador, you will play a vital role in promoting the effective use of learning technologies to enhance the educational experience of students.

Responsibilities include:

  • Engage new students with our technologies by sharing individual experiences during face-to-face Welcome Week inductions.
  • Deliver student inductions during the Welcome Week, alongside a member of the TEL Team. This includes a present of approximately 50 minute delivered to groups of up to 100 students, providing demonstrations, and addressing any queries or concerns raised by students.
  • Actively promote the benefits and features of learning applications supported by the TELT during presentations, emphasising how these technologies can enhance the learning experience.

Benefits:

  • Valuable experience in public speaking and presentation.
  • Opportunity to represent the student voice in the development of digital learning initiatives.
  • Training provided in the delivery of the TEL induction programme.
  • Opportunity to enhance your CV and develop transferable skills.
  • 13.99 per hour, paid on a weekly basis.


Qualifications

CURRENTLY ENROLLED AS A QMUL STUDENT AND WILL BE ENROLLED 24/25 ACADEMIC YEAR



Skills

Requirements:

  • Studied at Queen Mary during the 2023/24 academic year.
  • Enrolled to continue studying at Queen Mary for the 2024/25 academic year.
  • Passionate about technology enhanced learning and its potential to improve student experience.
  • Strong communication and interpersonal skills.
  • Excellent public speaking and presentation skills, with the ability to engage and connect with diverse audiences.
  • Strong interpersonal skills, with the ability to build rapport and effectively communicate with students and colleagues.
  • Willingness to work collaboratively with other student ambassadors and the TEL Team.
  • Ability to pick up new technologies in short order.
  • Available to attend online training during week commencing 26th August 2024. (Exact date to be confirmed)
  • Available to attend in-person training on campus during week commencing 2nd September 2024. (Exact dates and times to be confirmed)
  • Available to work during Welcome Week (16th ? 20th Sep), Monday to Friday, between the hours of 9-5pm (exact hours to be confirmed)

 

If you are interested in this exciting opportunity, please submit your CV and a cover letter.

Applications without a cover letter will not be considered.

 

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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



Qualifications

Currently enrolled at QMUL 



Skills

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 

We are seeking a Research Assistant with expertise in web development and implementation to support the enhancement of a digital resource currently under development. This resource, based on cutting-edge linguistic research, focuses on motion verbs in Latin and Ancient Greek, making complex linguistic structures more accessible and engaging for secondary school students and teachers. The role involves refining and optimizing the existing platform, ensuring a user-friendly experience, and integrating additional linguistic data.

This is an exciting opportunity to contribute to a meaningful educational and research-driven project that connects classical languages with digital innovation.

Please note that compensation includes payment for regular remote meetings.



Qualifications
  • A background in Informatics or Computer Science is required, with higher education qualifications being an advantage.


Skills
  • Proficiency in JavaScript and Python
  • Experience with web development and digital tools for education
  • Strong problem-solving skills and ability to work independently
  • Interest in linguistics, digital humanities, or educational technology (preferred but not required)

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.


Qualifications

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



Skills
  • 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.
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