Job description
This is an exciting opportunity for an ambitious Hydrogen System Engineer to fast-track their career development as a Knowledge Transfer Partnership (KTP) Associate, utilising skills in Mechanical Engineering, Chemical Engineering and Process Engineering with a specific focus on Thermodynamics Cycle, Electrolysis and Data Analysis. The post will be based at the company’s offices in Westhill, Aberdeenshire.
PURPOSE OF POST:
• Transfer knowledge of Organic Rankin Cycle, Electrolysis, Integration
techniques and
technologies to Interventions Rental
• Take a leading role in developing the Digital Twin system for Waste
Heat Harvesting
• Manage the KTP project and effectively communicate its purpose and
benefits, and guidelines
for system maintenance, usage and enhancement
• Gain insights from engineering projects using LLMs and integrate this
knowledge into
accessible workflows through CBR for effective knowledge management
PRINCIPAL DUTIES:
• Deliver the project objectives as detailed in the KTP project
workplan.
• Maintain an up-to-date project plan and provide regular progress
reports.
• Deliver presentations to immediate project team members and other
stakeholders.
KNOWLEDGE/EXPERIENCE
At least a 2.1/Merit MEng/MSc in Mechanical, Chemical or Process Engineering (a PhD in a relevant field would be desirable). Relevant industrial placements or previous employment is also highly desirable along with excellent knowledge of the thermodynamics cycle and process integration, and some knowledge of hydrogen production, storage and transport. An interest in computing/computer science is essential and prior knowledge of machine learning techniques is desirable.
Project description
To develop and commercialise a fully integrated system producing both electricity and green hydrogen from waste, heat. This innovative solution will incorporate machine learning, allowing the Company to be the first to enter the energy transition market with a system that can be optimised for different scales and operational conditions.
About the business
Intervention Rentals (IR) specialises in supplying iron rentals to the upstream & downstream energy sectors, catering to drilling/extraction (60%), marine (5%), production (34%), and geothermal (1%) industries. Revenue is primarily generated through a rental model requiring minimal maintenance.
Energy
Engineering
MEng/MSc in Mechanical, Chemical or Process Engineering (2.1/Merit). A PhD in a relevant field would be desirable.
11 September 2024
6 October 2024
RGU07108
This is an exciting opportunity for an ambitious Hydrogen System Engineer to fast-track their career development as a Knowledge Transfer Partnership (KTP) Associate, utilising skills in Mechanical Engineering, Chemical Engineering and Process Engineering with a specific focus on Thermodynamics Cycle, Electrolysis and Data Analysis. The post will be based at the company’s offices in Westhill, Aberdeenshire.
PURPOSE OF POST:
• Transfer knowledge of Organic Rankin Cycle, Electrolysis, Integration techniques and
technologies to Interventions Rental
• Take a leading role in developing the Digital Twin system for Waste Heat Harvesting
• Manage the KTP project and effectively communicate its purpose and benefits, and guidelines
for system maintenance, usage and enhancement
• Gain insights from engineering projects using LLMs and integrate this knowledge into
accessible workflows through CBR for effective knowledge management
PRINCIPAL DUTIES:
• Deliver the project objectives as detailed in the KTP project workplan.
• Maintain an up-to-date project plan and provide regular progress reports.
• Deliver presentations to immediate project team members and other stakeholders.
KNOWLEDGE/EXPERIENCE
At least a 2.1/Merit MEng/MSc in Mechanical, Chemical or Process Engineering (a PhD in a relevant field would be desirable). Relevant industrial placements or previous employment is also highly desirable along with excellent knowledge of the thermodynamics cycle and process integration, and some knowledge of hydrogen production, storage and transport. An interest in computing/computer science is essential and prior knowledge of machine learning techniques is desirable.