Postgraduate Engineer Apprenticeship Level 7

Currently employed

The potential apprentice will be required to complete an Initial Needs Assessment (INA) with the support of their employer and the University to ensure the Apprenticeship programme is an appropriate solution to their skills development needs. On completion of the INA the University will assess the information provided and determine the next steps. 

To request the Initial Needs Assessment please contact apprenticeships@plymouth.ac.uk

Seeking a new position

If you are currently not working, are a school leaver or are looking to change your job in order to start an Apprenticeship, you will need to apply for an Apprenticeship vacancy. You can find out about vacancies in a number of ways; the government offers an Apprenticeship vacancy search at: www.findapprenticeship.service.gov.uk/apprenticeshipsearch or you can contact us and we will direct you to a partner in your area who can support you with this process.

Fees and funding

  • Apprentices do not pay any course fees.
  • By studying a Degree Apprenticeship, students will obtain a full honours degree, and not incur any costs.
  • Fees are paid for by the employer.

Funding models

There are currently two funding models;

  • Co-funded: Non levy organisations and Levy who have exceeded their Levy contribution.
  • Fully funded: Levy paying organisation.

Programme cost

Total course cost £27,000

Example of costs per funding model:

Co-funded

If you are a non-levy paying organisation or you are a Levy organisation who has exceeded their pot the government will financial co-support your Apprenticeship training – they will contribute 95 per cent of the costs and as the employer you will contribute 5 per cent.

  • Government contribution £25,650
  • Employer contribution £1,350
  • Apprentice contribution £0

Fully funded

If the annual pay bill of your organisation exceeds £3m you will pay for your apprenticeship training through your levy account. If you have exceeded your levy contribution you will fund apprenticeship training through the co-funded model – to better understand how this will work please make contact with us.

Employer contribution £27,000

This programme primarily aims to recruit graduate engineers with degree to honours level at 2:2 or above in engineering, mathematics, robotics, marine, computing and physical science related subjects. Applicants without a 2.2 or higher may be considered if they have significant relevant experience. Ideally, candidates will need to demonstrate that they hold an appropriate IEng accredited first degree to comply with the full CEng registration requirements. Candidates with non-UK qualifications will need to demonstrate that their first degree is accredited by their local professional body. Candidates with considerable professional experience, who can evidence an academic ability equivalent to undergraduate degree levels, may also be considered.

Candidates will need to demonstrate their proficiency in Maths and English, e.g. grade C minimum at GCSE, AS Level, A Level, IB, Cambridge Proficiency Certificate Level 4-5, Oxford Higher Certificate, International Certificate Conference (ICC Stage 3 Technical), or IELTS. The minimum IELTS score for acceptable English proficiency for entry is normally 6.5 and not less than 6.0 in all components.

Programme summary

The MSc in Autonomous Systems Technology aims to provide further learning to Masters Level that will enable apprentice with background in engineering, mathematics, robotics, marine, computing and physical science related subjects to fulfil the educational requirements of the Chartered Engineer (CEng) Standard of the UK Standard for Professional Engineering Competence (UK-SPEC). The programme caters for a global demand of L7 degree apprenticeship with work-based learning for autonomy and will cover the specialist knowledge and skills necessary in autonomy such as artificial intelligence in decision-making, navigation, guidance, control and sensor fusion, machine learning, security, communication and networking and data management. It aims to provide a flexible and work-based route and combines a mixture of lectures, online learning, Webinar, assignments and other related study. In the first year of study, a key knowledge and skills in the autonomous areas will be developed along with learning contract between apprentices and employers. These skills will be necessary to complete work-based projects tailored to the specialist skills, knowledge and behaviour needed in a particular engineering role i.e. Systems Integration Engineer, Research and Development Engineer or Maintenance/ Test Engineer.

Key facts

  • ? 2.5 year programme (30 months)
  • @ 30-40 hours study per week
  • ? Blended delivery including online and day release
  • @ Programme running from September 2020

Benefits to the business

  • Prepares learners to be able to achieve the CEng through portfolio development and professional discussion.
  • Social and ethical issues related to autonomy.
  • New ways of thinking in the areas of perception and decision making leading to optimisation in the automation processes .
  • Knowledge of different type of AI based techniques which can be used to increase production, improve performances and reduce cost.
  • Unique programme in the UK, providing a strong skills set both in marine and robotic autonomy.
  • Added value to employer and learner around workplace assessment with the support from workplace mentor and academics with dedicated modules such as engineering design dissertation and end point assessment preparation.

Benefits to the apprentice

  • Flexible study hours with one to one support by visits at work place.
  • PG apprenticeship end point assessment preparation through a dedicated module.
  • Use of modern software and toolboxes for studies and experiments.
  • Achieve a career-enhancing postgraduate degree while earning a salary. No cost to pay as the government or your employer will pay your tuition fees. 
  • Prepares learners to be able to achieve the CEng through portfolio development and professional discussion.
  • Learn from world leading experts recognised nationally and internationally for high-quality research-led education.
  • Gain the skills, knowledge and experience needed to make you more competent at your workplace and help you to effectively manage complex engineering projects and to become an independent lifelong learner.
  • Get technology-enhanced learning by using our modern laboratories, engineering workshops, and high-quality library & digital learning and information environment with access to the University's student support services and community.

Core modules

Core modules (180 Credits):

Introduction to Autonomous Systems (40 credits) – This module will develop knowledge and understanding of data analysis, networking, communication and artificial intelligence (AI) technologies and will explore the benefits of these in autonomous systems. Practical sessions will develop appropriate software skills.  

Data Processing, Simulation and Optimisation of Engineering Systems (20 credits) – This module will develop student knowledge and understanding of data processing techniques and will explore the benefits of these in engineering practice. Further study of simulation and optimisation techniques for engineering systems will provide the tools to enable the students to develop dynamic simulation of real engineering problems, including behaviour prediction and performance optimisation using MATLAB/Simulink.

Intelligent Sensors and Control for Autonomous Systems  (20 credits) – The module covers a range of classical and smart sensors and actuators which can be applied in interactive and automated system, with emphasis on several key sensors. This module introduces the basic and advanced concepts of linear and nonlinear modelling analysis and design of intelligent control systems. This module has a strong hands-on component. 

Engineering Research Methods and Professional Development (20 credits) – You will use design research (DRM) to plan your research project and select ethically suitable quantitative and qualitative research methods. Your progression towards professional registration will be appraised and plans made for continuous professional development (CPD). 

Modelling and Analytics for Data Science (20 credits) – This module gives students an understanding of modelling and analytics techniques for Data Science. It supplies modern data modelling tool boxes for making strategic decisions in a broad range of Business related practical situations. It offers a hands-on introduction to Bayesian inference and machine learning. 

Engineering Design Dissertation - Work Based Learning (60 Credits) – This module allows students to demonstrate the practical skills and knowledge of research methods to plan and implement high quality research. Students will carry out a substantial research investigation on a topic of their choice and report upon the aims, methodology, data analysis, interpretation, synthesis, and conclusions through a high quality, scholarly and professional write-up of the project. 

End Point Assessment Preparation (0 Credit) – This module is aimed at apprentices who will be undertaking an end point assessment (EPA) as a part of their completion of the PG Apprenticeship. This module will run across the two year programme and will assist apprentices and their employers to be effectively equipped for the EPA.

Core skills

Core skills the apprentice will achieve from this programme:

  • Advance autonomous capability by deepening knowledge and skills in artificial intelligence in decision-making, navigation, guidance, control and sensor fusion, machine learning, security, communication and networking and data management.
  • Broaden knowledge of new and emerging technologies and/or professional practice.
  • Challenge apprentices to solve complex or novel engineering problems through projects that are practice-based.
  • Develop professional competency, work place readiness, and personal self-development skills.
  • Develop the skills necessary to pursue academic research and scholarship in engineering at a high level.

Areas of specialism

Areas and industry sectors apprentices can specialise in:

  • Analytics techniques for data science and machine learning.
  • Understanding of the core security mechanisms (e.g. data protection mechanisms)
  • Expertise in the use of MATLAB software and its associated toolboxes to solve a range of autonomy related problems.
  • Broad knowledge of Artificial Intelligence Techniques and their use in modelling, control, optimisation and multi-sensor-data-fusion.

This programme will specialise people in the areas of autonomy which is required now a days in the day to day working of any industries such as how to handle large data securely and work with better techniques to analyse and transfer in a faster way, create virtual model of the dynamic system and then analyse its performance and suggests improvement, fault detection and diagnostic to take timely actions and use of neural networks, fuzzy logic, wavelet techniques and other current technologies in optimisation, modelling, control, sensor fusion, estimation and redundancy. Modern engineering organisations require their employers to demonstrate a set of knowledge, skills and behaviours that will ensure success, both in their role and in the overall company objectives. These are aligned with the current edition of the UK Standard for Professional Engineering Competence (UK-SPEC) at Chartered Engineer (CEng) level. Two specific apprenticeship occupations which will be covered by this programme are: 1) Research and Development Engineer and 2) Systems Integration Engineer.

Assessment

Each module is assessed by one or more elements of assessment. The formally approved Module Record defines the proportion of the module’s assessment derived from each element and this cannot be amended without following due process. Each element of assessment may contain more than one component of assessment, the results of which are aggregated together to produce a single percentage mark or pass/fail assessment. The elements of assessment may include coursework (C1), examination (E1), test (T1), practical (P1), and a generic assessment element (A1). The MSc degree programme complies with the Assessment Policy in only requiring two pieces of summative assessment for each 20-credit module, except where a professional development requirement needs to be satisfied. 

Apprentices will be mostly assessed through coursework and practical assignments, with some time-constrained examinations. 

The following classifications of coursework are generally used:

  • Written assignment, including essay.
  • Report. A description, summary or other account of an experience or activity.
  • Dissertation. An extended piece of written work, often the write-up of a final-year project.
  • Portfolio. Typically, a portfolio contains a number of pieces of work, usually connected by a topic or theme, and often includes some reflective accounts (diaries/logs). 
  • Project output. Output from project work, often of a practical nature, other than a dissertation or written
  • Set exercise. Questions or tasks designed to assess the application of knowledge, analytical, problem-solving or evaluative skills. This includes tests (written or computer-based) of knowledge or interpretation that are not conducted under examination conditions.

Practical assessments include:

  • Oral assessment and presentation, e.g. conversations, discussions, debates, presentations, individual contributions to seminars, and viva voce exam.
  • Practical skills assessment focuses on whether, and/or how well, an apprentice performs a specific practical skill or technique (or competency).

People

*UK-SPEC: UK Standard for Professional Engineering Competence. The Engineering Council, 3rd Edition, January 2014.