Student Intern

British Sign Language teaching for medical students, Student Selected Component. 

British Sign Language teaching qualifications.

Experience teaching British Sign Language and teaching at King's College London. 

THE TEAM ARE HIRING ONE FULL-TIME AND ONE PART-TIME (15 HOURS) POSITION. 

PLEASE SPECIFY WHICH YOU ARE INTERESTED IN IN YOUR APPLICATION. 

School/Department: Schoolof The Built Environment and Architecture

Job Title/Grade: Apprenticeship Skills Reviewers (Civil and Building Services) ? Grade 6

Reporting to: Apprenticeship Team Leader

Purpose of the post

The School of The Built Environment and Architecture (BEA) offers some of London?sbest

courses in areas such as architecture, building services engineering, civil engineering, construction management, quantitysurveying and buildingsurveying. All our courses are closely linked with industry and accredited by the relevant professional institutions to the highest possible level.

Our apprenticeship offer is rapidlyexpanding and we are seeking passionate and driven Skills Reviewers who are committed to working with our academics and industry partners to develop the best Higher and Degree Apprenticeships in the sector. This particular post will sit within the division of Civil and Building Services.

The postholder will provide guidance and support to apprentices to work towards developing the knowledge, skills and behaviours they will need to successfully complete their apprenticeship, and will liaise with employers to ensure the apprentice?s learning needs are being met in the workplace.Progress review meetingswith each apprentice and their employer will be undertaken at least three times a year for the duration of their programme, either in person at the apprentice?s workplace, or remotely via MS Teams.

The successful candidate will be an excellent communicator and developing good relationships with internal and external stakeholders. Industry experience would be an advantage. They will be comfortable working independently and will have excellent organisational and time management skills.

Please note we are willing to consider applications for part-time hours.

MAIN ACTIVITIES AND RESPONSIBILITIES

1. Work alongside both the Apprenticeship Team and School academics to provide proactive and reactivevocational and educational support to apprentices and their line managers throughout the apprenticeship scheme.

1. Monitor apprentice progression against knowledge, skills and behaviour standards (as well as functional skills where appropriate) and provide feedbackto both the apprentice and their line manager.

1. Undertake regular progress review meetings with apprentices and mentors as required. These meetingswill either take place at the apprentice?s workplace; the University; or via MS Teams.

1. Support the apprentice to prepare for theirend-point assessment.

1. Work with employersand academic colleagues to support the agreement of on and off the job development plans with the apprentices and monitor and record progress to ensure that both the university and employer remain compliant.

1. Work with the Apprenticeship Team and Teaching, Quality and Enhancement to ensure all qualityassurance requirements relevantto the educational support of an apprentice are being met.

1. Ensure that all learning activity is recorded and auditable in line with funding regulations and that any university delivery, process and monitoring systemsare adhered to within the agreed time limits.

1. Contribute to the development of appropriate programme systems and processes.

1. Complete any requiredlearning and development activities required to enhance your knowledge and perform the role more effectively.

1. Ensure that procedures for access to, and retention of, information withinthe School comply with the requirements of the University and the General Data Protection Regulation.

The Apprenticeship Team Leader / Dean may requestadditional duties that are withinthe scope, spirit and purpose of the role. Within reason, the duties and role of the post holder may be changed, after appropriate consultation, in response to changing organisational requirements.

N/A 

SELECTION CRITERIA

E = Essential, D = Desirable

1. A relevantconstruction qualification (D). Industry experience or professional qualifications relating to the apprenticeship discipline (D).

1. An understanding of the end-to-end learning journey of an apprentice (D)

1. Experience of delivering work based learningand assessment in commercial or educational contexts which are relevant to the subject area (D).

1. Excellent communication skills and the ability to build customer/ client relationships and maintain empathy with stakeholders (E).

1. Experience of working autonomously and managing own workload efficiently and effectively (E).

1. Ability to work as part of a wider team (E).

1. Excellent time management and organisational skills (E).

1. Experience or knowledge of  e -learning platforms and an excellent knowledgeof Microsoft Office packages, including the ability to produce reports (E).

1. Ability to demonstrate an understanding of, and commitment to, equality and diversity, and its practical application (E).

The successful candidatewill be required to undergoan Enhanced DBS check.

• 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