Visit with all Jobs for Academics!
Position: Research Scientist - Machine Learning and Artificial Intelligence (KTP Associate)
Institution: University of Manchester
Department: Computer Science
Location: Manchester, Greater Manchester, United Kingdom
Duties: Manage the project independently, including project planning, running monthly project meetings and liaising with the academic team. Acquire background knowledge for the project and establish contact with key industry stakeholders to understand the market needs, analyse the requirements, and identify commercial opportunities. Develop and apply knowledge and experience of machine learning and AI techniques to design/select appropriate prediction models and develop/implement these algorithms based on the available data set and requirements. Develop scientific metrics, analytics, and insights generators to assess location and service offers, understand market competitiveness, and forecast sales
Requirements: PhD degree in Machine Learning, AI, Statistics, Applied Mathematics, Operations Research, Computer Science, Economics or a related engineering/science degree is preferred. Individual must have skills and experiences in the following areas: High proficiency in Machine Learning, AI, and Statistical methods, as well as good knowledge in mathematical programmers and heuristic optimization; High proficiency in programming with a scientific software language – preferably R; Basic experience in programming with SQL and using Microsoft SQL Server Management Studio; Demonstrate a good level of creativity in design of solutions. Ability to work both independently and as part of a team with industrial partners and academic researchers. Ability and desire to understand the business domain and engage with non-technical staff and clients. Strong communication skills, including the ability to explain technical concepts in layman's terms. Excellent time management and organisational skills and ability to meet deadlines. Willingness to learn and ability to fill in knowledge gaps independently
Text: Job Reference : S&E-13868 Location : Kalibrate Closing Date : 28/05/2019 Salary : £32,236 to £39,609 per annum according to experience a Personal Development Budget totalling £4,000 Employment Type : Fixed Term Faculty / Organisational Unit : Science & Engineering Division : Computer Science - Machine Learning and Optimisation Hours Per week : Full time Contract Duration : 24 months This is an exciting and unique opportunity for an ambitious recent PhD graduate or post-doctoral research scientist with the ability and confidence to manage a strategically important Knowledge Transfer Partnership (KTP) project with Kalibrate Technologies Limited. As a market leader in fuel pricing and network planning technologies, Kalibrate develops and delivers strategy and technology solutions to drive greater value from the fuel and convenience retail chain. Today, the automotive industry is going through its biggest transformation from combustible engines to vehicles that run on alternative fuels. The leading alternative fuel within this industry is electric charging. With this evolution underway, this KTP project creates a distinguishing and exciting opportunity for you to develop a new generation of scientific methods to fill in the technical and market gap needed for the infrastructure development and deployment of electric vehicle charging facilities. From an individual professional development point of view, this project provides a rare and unique opportunity for you to become the first generation of experts within the new car energy retail market. We are looking to recruit a research scientist to undertake this 24 month project which has an overall aim of developing and embedding machine learning and artificial intelligence techniques to create predictive network planning and location selection models capable of determining optimal infrastructure needs for electric vehicle charging facilities, based on projected demand. The position will provide you with a unique opportunity to develop and apply state-of-the-art machine learning and artificial intelligence techniques methods to address a cutting edge business problem which has a high level of commercial applicability. You will play a vital role in introducing new computational and decision analysis techniques to support strategically important future business development. The position is particularly suitable if you want to bridge academic and industrial research excellence. You will be part of a collaborative development and knowledge-transfer project between The University of Manchester and Kalibrate. You will not only receive formal management training but will also have access to a £4,000 personal and professional development budget. Based at Kalibrate Corporate HQ on Deansgate in Manchester, you will work directly with supervisors from both the University and Kalibrate and will use the facilities and resources of both organisations. The School is committed to promoting equality and diversity, including the Athena SWAN charter for promoting women’s careers in STEMM subjects (science, technology, engineering, mathematics and medicine) in higher education. The School holds a Bronze Award for their commitment to the representation of women in the workplace and we particularly welcome applications from women for this post. All appointments will be made on merit. For further information, please visit: Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies. Enquiries about the vacancy, shortlisting and interviews: Name: Dr Xiao-Jun Zeng or Dr Tingting Mu Email: ; General enquiries: Email: Tel: 0161 275 4499 Technical support: Email: Tel: 0161 850 2004 This vacancy will close for applications at midnight on the closing date. Please see link below for the Further Particulars document which contains the person specification criteria that you should address in your application. S&E-13868 Research Scientist - Machine Learning and Artificial Intelligence (KTP Associate) FPs ; Back
Please click here, if the Job didn't load correctly.

Please wait. You are being redirected to the Job in 3 seconds.