Machine Learning Data Scientist KTP Associate Birmingham, England
Job description
This role is an exciting opportunity for candidates with a specialist MSc in Computer Science or Mathematics based subject involving mathematics and computing. The ideal candidate will have a strong foundation in machine learning techniques and optimisation algorithms.
This KTP project aims to create a system that centralises social housing residents' job requests and pushes them directly to contractors ensuring:
- Contractors' skills are used optimally.
- Residents are visited by relevant contractors in an appropriate order at a convenient time.
- Dynamic priorities of jobs are taken into account, e.g. scheduled jobs become more urgent/ less important relative to their initial priority. The intended system should respond to such changes in real-time, by automatically reassigning contractors accordingly.
This project will help Thames Labs to optimise its interactions with its supply chain to reduce back-office and field operator costs, thereby improving profitability and productivity to enable its continued growth trajectory.
Candidate Profile: MSc (Merit or above) in Computer Science, Mathematics, Artificial Intelligence, Data Science or another subject involving mathematics and computing.
Key skills required: The Associate should be able to demonstrate their numerical and machine learning software skills in appropriate programming packages e.g. MATLAB/Python.
Experience of the following techniques would be a distinct advantage:
- Supervised and adaptive/ unsupervised machine learning techniques
- Multi-criteria optimisation techniques
- Software architecture design
- Deterministic and non-evolutionary stochastic methods
- Computational intelligence and applications of evolutionary computation
Personal attributes:
- Excellent verbal and written communication skills.
- Proactive attitude to manage day-to-day challenges encountered in working in a technology-based industry, finding original solutions that will be crucial to deliver the project with the necessary high levels of innovation
- The Associate should be able to demonstrate the capability to work both independently and collaboratively and have a proven track record of effective interaction with colleagues in industry and academia.
- Good project management skills are required with the ability to develop work plans under own initiative and work to deadlines.
- Strong skills in documentation, report writing and presenting for a range of audiences will be necessary.
Benefits:
- Up to £5000 for personal and professional development for the duration of the project.
- Ashorne Hill Management and Ledership Training
- Pension provision
- Possibility of a hybrid work arrangements
This is a Knowledge Transfer Partnership (KTP) funded by Thames Laboratories and Innovate UK-KTN. It is essential you understand how KTP works and the vital role you will play if you secure this position. To learn more please visit: www.aston.ac.uk/ktp
The Company: The Hazard Management and Environmental Services Laboratories Ltd trading as Thames Laboratories (TL) is primarily an asbestos consultancy supplying surveys, project management and training across the UK. These services are provided to businesses, local authorities, social housing and education facilities using a fleet of mobile engineers. On average completing in excess of 25,000 individual jobs per year for in excess of 150 Clients across nearly 500 programmes of works. More information: https://www.thameslabs.co.uk/
Aston University: You will work in a project team with Dr Alina Patelli,
Prof Aniko Ekart from Aston University and the Senior Management Team at Thames Labs with the support of the IUK-KTN’s Knowledge Transfer Adviser.
Location: You will be based predominantly at Thames Laboratories in Fenstanton but will also have access to facilities at Aston University in central Birmingham. Some travel to key clients may also be required.
For informal enquiries about this role please contact Dr Alina Patelli, Senior Lecturer, Informatics and Digital Engineering, E-mail: [email protected]
