KTP Associate: Machine Learning/Computational Modelling Engineer

University of Bath; Bath; On-site; GBP 35,000 - 45,000; Full time

Benefits offered by this job

Pension scheme

Cycle to work

Electric vehicle salary sacrifice

Staff gym and discounts

Professional development opportunities

Tuition and language courses

Job summary

ioMosaic is seeking a KTP Associate in Machine Learning/Computational Modelling for a 29‑month, full‑time placement based at Bath. The role blends AI for chemical engineering to enhance process safety and sustainability, with academic support from the University of Bath.

You will lead the project at ioMosaic, collaborating with university and industry teams to develop a tool for decision‑making in process safety engineering and advance your career.

Qualifications

• This role requires a Masters or PhD in Chemical or Mechanical Engineering.
• Experience or strong interest in programming, modelling, or simulation (Python, MATLAB, C++, or similar).
• Familiarity with numerical or simulation tools such as ANSYS, ASPEN, COMSOL, or OpenFOAM.
• Proven skills in AI and machine learning.

Responsibilities

• Lead the KTP project, coordinating with ioMosaic, University of Bath and other stakeholders.
• Develop AI/ML based tools to aid decision-making in process safety engineering.
• Contribute to knowledge transfer between academic and industrial partners.

Skills

Programming

AI/ML

Numerical simulation

Communication skills

Education

Masters or PhD in Chemical or Mechanical Engineering

Tools

Python

MATLAB

C++

OpenFOAM

Job description

KTP Associate: Machine Learning/Computational Modelling Engineer

Job title KTP Associate: Machine Learning/Computational Modelling Engineer

Department Chemical Engineering

Salary Starting from £35,000, rising to £45,000

Do you have a strong interest in using artificial intelligence and machine learning techniques in chemical engineering to create a safer and more sustainable work environment in the oil, gas, petrochemical and chemical industries?

Do you want to further your career through a project with one of the UK’s leading research-intensive universities whilst pursuing a career in industry?

We are seeking an Engineer in Machine Learning or Computational Modelling for a Knowledge Transfer Partnership (KTP) Associate position based at ioMosaic International Ltd in Bath.

About the role

This Knowledge Transfer Partnership will bring together an exciting combination of expertise in artificial intelligence and chemical engineering to deliver a new tool for decision-making in process safety engineering. The tool will deliver a step-change in the design of processing plants, improving safety and reducing environmental impact.

You will work within ioMosaic Corporation to embed knowledge through this innovation project, with research and academic support from the University.

This is an opportunity for you to ‘fast track’ your career into business by leading a high profile and strategically meaningful project to a successful conclusion, allowing you to utilise your academic achievements in an industrial setting.

By joining this KTP between ioMosaic Corporation and the Departments of Chemical Engineering and Computer Science at the University of Bath you will have the opportunity to work on challenges, using ground‑breaking technology with incredible team support and development opportunities.

ioMosaic Corporation is a rapidly growing company providing consulting services in the areas of process safety and risk management in the oil, gas, petrochemical and chemical industries. Based at ioMosaic in Bath, you will be a member of a wider multidisciplinary team, as well as working with the software project team in the UK and US.

The ideal candidate will have:

• A Masters or PhD level qualification in Chemical or Mechanical Engineering.
• Experience or strong interest in programming, modelling, or simulation (e.g. Python, MATLAB, C++, or similar platforms).
• Familiarity with numerical or simulation tools such as ANSYS, ASPEN, COMSOL, or OpenFOAM.
• Proven skills in AI and machine learning.

You will play a major role in the KTP and engage with multiple stakeholders, so must have excellent communication and organisational skills.

About KTPs

As a KTP Associate you will be the project lead, managing your own workload to ensure outputs align with project expectations, including leading on meetings. You will also acquire new skills in strategic leadership that can help to super‑charge your career.

Additional benefits of being a KTP Associate:

• All KTP Associates spend 10% of their time on Personal Development, making full use of a generous £2,000 annual personal development budget.
• Access to two 1-week residential training sessions at Ashorne Hill, aimed at developing project management skills.
• Potential to fast‑track a career into business, with an opportunity to gain a permanent job following the KTP.
• Access to a wealth of academic and industrial resources.
• Access to a Travel & Subsistence budget.
• Access to mentoring and coaching sessions through Innovate UK Business Connect.

As a KTP Associate you can enjoy the benefits of being an employee at the University of Bath whilst working full‑time at ioMosaic.

This is a full‑time in‑office role based at the company offices in Bath and the University of Bath.

You will be entitled to 25 days paid annual leave in addition to UK public holidays. Working hours are 8:45am to 5:30pm Monday to Friday.

This is a fixed‑term role for a period of 29 months.

There will be a two stage interview process: an initial online screening and an in‑person interview at the ioMosaic office in Bath, in January 2027.

We consider ourselves to be a university where difference is celebrated, respected and encouraged. We have an excellent international reputation with staff from over 60 different nations and have made a positive commitment towards gender equity and intersectionality receiving a Silver Athena SWAN award. We truly believe that diversity of experience, perspectives, and backgrounds will lead to a better environment for our employees and students and encourage applications from all genders, backgrounds, and communities, particularly from under‑represented groups, and value the positive impact that will have on the university. We are committed to maintaining a safe and secure environment for our students, staff, and community by reinforcing our Safer Recruitment commitment.

We’re continually expanding our benefits package to better support you and enhance your experience with us and the below is just an example of some of the many great benefits we offer:

• Generous employer contributory pension schemes
• Cycle to work scheme
• Electric vehicle salary sacrifice scheme
• Staff discount at Team Bath gym
• Staff benefits include discounted postgraduate tuition and language courses, alongside a wide range of personal and professional development opportunities such as apprenticeships, LinkedIn Learning, and more
• Local discounts, including free entry to the Holburne Museum in Bath
• Employee assistance services through Health Assured, including access to the Wisdom app

We’re committed to creating a health-supporting working environment where everyone can thrive and be at their best. We offer a range of resources to enable you to do that from your first day here, including:

• Staff support groups and networks
• A network of Staff Wellbeing Champions
• A suite of tools, resources and frameworks that enable you to be at your best and thrive

Right to Work in the UK:We do not assess immigration or right to work status until offer stage. Being invited to interview does not guarantee you are eligible for visa sponsorship and all offers are conditional on meeting right to work requirements. Further information is available on our application support webpage.

Anonymous shortlisting:We are constantly seeking to reduce the unconscious bias that enters any assessment process, with the goal of creating an inclusive and equal assessment process. To support this, personal details such as your name, may be removed from application forms at the initial shortlisting stage.