- Various RGU partner locations, AB10 7AQ
- £35,000 per year
- Contract Type:
- Fixed Term
- Position Type:
- Full Time
- Work From Home:
This is an exciting opportunity for an ambitious graduate in Artificial Intelligence/ Computer Vision (AI/CV), or a strongly related field, to fast-track their career development as a Knowledge Transfer Partnership (KTP) Associate. KTP is the UK’s oldest knowledge transfer programme, supporting partnerships between business and universities or research organisations, placing talented graduates (KTP Associates) to work on innovative, high-profile projects. KTPs are part grant-funded by Innovate UK, the United Kingdom's innovation agency, which provides money and support to organisations to make new products and services on behalf of the UK Government.
The KTP Associate will undertake a 2-year collaborative project between Energy Services International (Eserv) and Robert Gordon University’s (RGU) School of Computing. The post will be based at the company premises (70 Queens Road, Aberdeen) and will also have access to RGU’s research facilities. You will develop novel capabilities for Eserv’s state-of-the-art Digital Twin platform, AS-TEG™. These will help to automate traditionally manual, expensive, timely and emission-creating tasks using AI/CV. These include:
- Digitisation of paper and unannotated complex engineering drawings, using object detection and pattern recognition techniques.
- Implementation of novel algorithms to develop customer-led functionality to interact with the digitised data and digital twins.
- Development of robust and multi-modal defect recognition models to detect corrosion and other types of site faults.
You will receive extensive practical and formal training, gain marketable skills, broaden your knowledge and expertise within an industrially relevant project, and gain valuable experience from industrial and academic mentors. You will benefit from a Personal Development Budget of £4,000.
Candidates must ideally possess an Honours Degree in Computer Science with a focus on AI/CV. However, those with a 1st Class Honours Degree in related disciplines should also apply for this post. You will be expected to relocate to Aberdeen, Scotland, and should be self-motivated, being able to work independently and to tight deadlines within a dynamic team environment. It is desired, but not essential, to be experienced or familiar with the Energy sector.
Excellent communication and interpersonal skills are required, as the ideal candidate must be able to communicate effectively with various individuals, i.e., technical, academic, business and customers. Team working and flexibility will be essential requirements.
Salary Range: Up to £35,000 with a review after one year, plus £4,000 training budget
This post is subject to a Disclosure Scotland check. For more information visit: https://www.mygov.scot/basic-disclosure/
About the company
Eserv works within the Energy sector and has developed an industry-leading software-as-a-service (SaaS) solution that revolutionises how customers design, build, operate and maintain complex industrial assets through its digital twin technology AS-TEG™. AS-TEG™ provides a contextualised digital twin that gives owners / operators and service providers the ability to quickly search and locate as-built, design and integrity data right from the desktop – from anywhere in the world.
RESPONSIBLE TO: Dr Carlos Moreno-García, School of Computing at RGU and Steven Simpson - Energy Services International (Eserv)
RESPONSIBLE FOR: No supervisory responsibilities
PURPOSE OF POST:
- Transfer knowledge of AI, CV and similar technologies to Eserv.
- Provide guidance as required to staff at Eserv.
- Deal with problems that may affect the achievement of project objectives and deadlines.
- Carry out administrative tasks related directly to the project delivery and knowledge transfer.
- Develop technical and personal skills (verbal and written) as required with increasing responsibility as experience level develops throughout the project.
- Deliver the project objectives as detailed in the KTP project proposal.
- Undertake assessment of the company.
- Explore, understand, and critically evaluate existing products/processes at Eserv.
- Take a leading role in developing and evaluating technical challenges from existing methodologies, processes map, and methodologies report.
- Technical requirements, challenges & recommended solutions report.
- Maintain an up-to-date project plan and provide a progress report for presentation at regular Local Management Committee (LMC) meetings.
- Deliver presentations to immediate project team members and technical experts.
- Any other duties that may be reasonable, assigned by the Academic Supervisor/ Company Supervisor.
First-class Honours degree in computing, data science or a strongly related discipline
Knowledge and skills
Strong knowledge of machine learning, computer vision and the underlying theories (pattern recognition, feature extraction and mapping, graph representations, deep learning, deep sequence models, time series analysis, etc.).
Self-motivated with an ability to work independently and to tight deadlines within a dynamic and team environment.
Ability to undertake research and development analysis.
Willingness and ability to learn quickly.
Excellent communication, report-writing and interpersonal skills: must be able to communicate effectively with various individuals from different backgrounds i.e. technical, academic, business and customers.
Ability to make informed decisions in a changing environment.
Able to present in both written & verbal form, with experience in delivering presentations to wider audiences.
Strong programming skills (e.g., Python, R, Matlab, Go, etc.) and scripting skills
Masters or PhD degrees in related topics.
Knowledge of PHP and SQL.
Knowledge of packages used for machine learning (e.g., Scikit-learn, OpenCV, Keras, TensorFlow, PyTorch, etc.)
Interest in and commitment to supporting business growth opportunities in Aberdeen.
Understanding of remote inspection or Oil & Gas related practice.
Track record of scientific publications in related journals and conferences (e.g., IEEE, CVPR, others).
Experience in managing projects covering multiple aspects is desirable.