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- Tasks: Develop an AI-powered scheduling framework for the chemical manufacturing industry.
- Company: Join a leading university collaborating with top academics and industry partners.
- Benefits: Enjoy a fantastic pension scheme, health services, generous leave, and local discounts.
- Why this job: Be part of innovative projects that make a real impact in manufacturing and sustainability.
- Qualifications: Strong background in process systems engineering, machine learning, and optimisation required.
- Other info: Flexible hybrid working arrangements available; equal opportunities employer.
The predicted salary is between 28800 - 48000 £ per year.
Overall purpose of the job:
This position is to deliver a next generation autonomous online scheduling framework in response to different types of disruptions in the chemical manufacturing industry using machine learning techniques. You will be responsible for the evaluation of energy consumption for industrial data reconciliation and preparation of process scheduling models, quantification of different types of uncertainty and the development of data-driven autonomous techniques for online scheduling. You will collaborate seamless with academics from University College London for such development. You will also work closely with the industrial partners to test the new online scheduling framework in a practical context and demonstrate the benefit.
The position requires strong expertise in process systems engineering, mathematical modelling, optimisation, machine learning, and artificial intelligence.
What you will get in return:
- Fantastic market leading Pension scheme
- Excellent employee health and wellbeing services including an Employee Assistance Programme
- Exceptional starting annual leave entitlement, plus bank holidays
- Additional paid closure over the Christmas period
- Local and national discounts at a range of major retailers
As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Our University is positive about flexible working – you can find out more here
Hybrid working arrangements may be considered.
Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.
Any recruitment enquiries from recruitment agencies should be directed to People.Recruitment@manchester.ac.uk . Any CV\’s submitted by a recruitment agency will be considered a gift.
Enquiries about the vacancy, shortlisting and interviews:
Name: Dr Jie Li
Email: jie.li-2@manchester.ac.uk
Or
Name: Dr Dongda Zhang
Email: Dongda.zhang@manchester.ac.uk
General enquiries:
Email: People.recruitment@manchester.ac.uk
Technical support:
https://jobseekersupport.jobtrain.co.uk/support/home
This vacancy will close for applications at midnight on the closing date.
Please see the link below for the Further Particulars document which contains the person specification criteria. #J-18808-Ljbffr
Research Associate in Artificial Intelligence Powered Framework for OnLine Production Scheduling employer: The University of Manchester
Contact Detail:
The University of Manchester Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Associate in Artificial Intelligence Powered Framework for OnLine Production Scheduling
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning and artificial intelligence, particularly as they relate to process systems engineering. This knowledge will not only help you during interviews but also demonstrate your genuine interest in the field.
✨Tip Number 2
Network with professionals in the chemical manufacturing industry and academia, especially those connected to University College London. Engaging with them can provide insights into the role and may even lead to referrals.
✨Tip Number 3
Prepare to discuss specific examples of how you've applied optimisation and mathematical modelling techniques in past projects. Being able to articulate your hands-on experience will set you apart from other candidates.
✨Tip Number 4
Stay updated on current challenges in the chemical manufacturing sector, particularly those related to energy consumption and disruptions. Showing that you understand these issues will highlight your readiness to contribute effectively to the team.
We think you need these skills to ace Research Associate in Artificial Intelligence Powered Framework for OnLine Production Scheduling
Some tips for your application 🫡
Understand the Role: Thoroughly read the job description for the Research Associate position. Make sure you understand the key responsibilities, required skills, and the specific areas of expertise needed, such as machine learning and process systems engineering.
Tailor Your CV: Customise your CV to highlight relevant experience and skills that align with the job requirements. Emphasise your expertise in mathematical modelling, optimisation, and any previous work related to artificial intelligence or online scheduling frameworks.
Craft a Compelling Cover Letter: Write a cover letter that not only outlines your qualifications but also demonstrates your enthusiasm for the role. Mention your interest in collaborating with academics and industrial partners, and how your background makes you a suitable candidate for this position.
Proofread Your Application: Before submitting, carefully proofread your application materials. Check for spelling and grammatical errors, and ensure that all information is clear and concise. A polished application reflects your attention to detail and professionalism.
How to prepare for a job interview at The University of Manchester
✨Showcase Your Technical Skills
Make sure to highlight your expertise in process systems engineering, mathematical modelling, optimisation, and machine learning. Prepare specific examples of projects or experiences where you've successfully applied these skills, as they are crucial for the role.
✨Understand the Industry Context
Familiarise yourself with the chemical manufacturing industry and the types of disruptions it faces. Being able to discuss how your work can address these challenges will demonstrate your understanding and commitment to the role.
✨Prepare for Collaborative Questions
Since the position involves collaboration with academics and industrial partners, be ready to discuss your experience working in teams. Think of examples where you successfully collaborated on projects, especially in a research or technical environment.
✨Ask Insightful Questions
Prepare thoughtful questions about the online scheduling framework and its practical applications. This shows your genuine interest in the role and helps you understand how you can contribute effectively to the team.
