Brief of Open Positions
PhD Funded Studentship in Deep Neural Networks, Adaptation and Transparency Data Scientist (KTP Associate – Fixed Term) Academic Positions Research Fellow in Machine Learning and Deep Neural Architectures for Signal Analysis and Prediction Postdoctoral Research Fellow in Data Science and Control (Fixed Term) Research Assistant in Big Data (Part Time, Fixed Term) – INTERNAL ONLY
Details of Open Positions
Research Assistant in Big Data (Part Time, Fixed Term) – INTERNAL ONLY
School of Computer Science
Salary: From £27,285 pro rata
This post is part time at 0.5 FTE and fixed term until 31st August 2018.
Closing Date: Sunday 21 January 2018
Interview Date: Friday 02 February 2018
The Research Assistant will be a key player in the delivery of the UK Innovate-funded project ‘The Development of Dynamic Energy Control Mechanisms for Food Retailing Refrigeration Systems’. This post is based at the University of Lincoln, one of the partners of the project, led by Intelligent Maintenance Systems Ltd. Other partners are Tesco Stores Ltd and The Grimsby Institute (GIFHE). The aims of the project are a large scale demonstration of the application of firm frequency (FFR) demand side response (DSR) mechanisms during the control of a massive distributed network of food retailing refrigerators. During the life of the project, a large scale test of FFR DSR to a significant proportion of the Tesco food retailing refrigerating equipment will be undertaken. This is an exciting opportunity to join a multidisciplinary research team with strong links to industry. The Research Assistant will form part of a group of two other Postdoctoral Research Fellows delivering the project at the University of Lincoln. You will focus on the Big Data Analysis and its impact on how the system can be controlled in real time amongst huge incoming data set from 112,000 refrigerators dispersed across the UK. Applicants must have a good honours degree in Computer Science or Engineering related or a relevant analytical subject, and/or Masters degree, or near completion, in Data Science or Machine Learning. Applicants should have experience with computing techniques, such as statistical analysis, machine learning, data mining/compression and data visualization. Additional knowledge in Control systems and optimization would be an advantage. Skills and experience with big data tools such as Hadoop and Apache Spark, data loading tools such as Sqoop and Flume, and/or programming languages such as Python, R and C, and data transformation and processing in different environments and formats, are highly desirable. The post is fixed-term in line with the length of the research project, which concludes on 31st August 2018. Applicants wanting an informal discussion about the post can contact the Principal Investigator Prof. Simon Pearson (email@example.com) or the Co-Investigators Dr Georgios Leontidis (firstname.lastname@example.org), Dr Argyrios Zolotas (email@example.com) and Prof. Ronald Bickerton (firstname.lastname@example.org). This position does not fulfil the UK Visas & Immigration sponsorship criteria for Tier 2. Postdoctoral Research Fellow in Data Science and Control (Fixed Term)
School of Computer Science
Salary: From £32,548 per annum
This post is full time, fixed term until 31 October 2020.
Closing Date: Tuesday 05 December 2017
We are looking for a highly skilled and experienced Postdoctoral Research Fellow in the area of data science and control with a particular focus on mining food and agricultural data within a large EU-funded project. The Postdoctoral Research Fellow will be a key player in the delivery of part of this project titled ‘Big Data and eco-innovative resource use in the NSR Greenhouse industry – greening the growth in horticultural production’ and the post is based at the University of Lincoln. The overall project aims to promote a forward leap towards a more robust, sustainable and energy efficient greenhouse production. The work at University of Lincoln aims to establish a Big Data analytics platform for scientific discoveries and advanced applications of data mining and machine learning to the various dimensions of resource usage in the North Sea Region (NSR) greenhouse industry. The candidate should hold* a PhD in Computer Science, Engineering or in a relevant subject area (or equivalent industrial experience), with proven technical knowledge and hands-on experience of data science and machine learning related algorithms and tools as well as an appreciation of data analysis for systems and control purposes. Previous experience with Amazon Web Services and Databricks would be beneficial. The candidate should also possess excellent communication skills, team spirit and willingness and ability to work in a highly interdisciplinary environment. (*) Good candidates may be accepted with a PhD pending, subject to publication record. Research Fellow in Machine Learning and Deep Neural Architectures for Signal Analysis and Prediction
School of Computer Science, University of Lincoln, UK
Salary: From £32,004 per annum
This is a new post, for a fixed term of 27 months
Closing Date: Thursday 19 October 2017
Interview Date: Friday, 3 November 2017
We seek to employ a highly motivated post-doctoral researcher that will be working on the EU-H2020 project “CORe monitoring Techniques and EXperimental validation and demonstration (CORTEX)”. The project will give the opportunity to the post holder to engage with other partners of the project across Europe. You should hold a PhD (or equivalent) or be near to completion, and should be able to demonstrate a good track record in at least one of the following research fields: Machine Learning, Deep Learning, Signal Processing Applications, Pattern Analysis. The contract is fixed for 27 months. The starting time can be soon following the announcement of the successful candidate. This position is funded by the EU-H2020 project CORe monitoring Techniques and EXperimental validation and demonstration, which runs from September 2017 to August 2021. The CORTEX project aims at developing core monitoring techniques that can be used to detect and characterize operational problems, before they have any inadvertent effect on plant safety and availability. The project relies on using the fluctuations existing in any process parameter that can be measured at the plants in order to monitor their state, primarily the neutron flux. The postholder will work on applying machine learning and deep learning techniques for analysing the collected signals and produce effective and transparent predictions. You can apply on-line through the University of Lincoln web site. If you would like to know more about this opportunity, please contact Professor Stefanos Kollias (Founding Professor of Machine Learning, email@example.com) or Dr Georgios Leontidis (Lecturer in Machine Learning, firstname.lastname@example.org)
PhD Funded Studentship in Deep Neural Networks, Adaptation and Transparency A PhD position is available in the Machine Learning Research Group (http://mlearn.lincoln.ac.uk) at the University of Lincoln UK. The position is fully funded for 3 years, including a student bursary, tuition fees, and participation in international conferences and meetings. Please note: *this studentship is available to UK/EU candidates only*; at present we are not able to offer any studentships to international candidates. The selected candidate will work around the area of deep neural networks and deep learning for signal and/or big data analysis, especially related to adaptation and transparency issues in applications, such as healthcare and complex data processing. He/she will strongly benefit from the collaborations we have both within the University of Lincoln and internationally. You should have a good Bachelors or Masters degree in Computer Science, Engineering, Mathematics or Physics. You must have excellent mathematical background, and experience in related areas such as machine learning, deep learning and computer vision as all considered beneficial. You should be available to start work on the project in the autumn of 2017 (exact start date may be negotiable). In the first instance, please contact professor Stefanos Kollias (email@example.com), with your CV and transcript records. Or alternatively Dr Georgios Leontidis (firstname.lastname@example.org). Please put “PhD application – Deep Neural Networks” in the subject line. Applications will be assessed as they arrive and, if appropriate, we will contact applicants to discuss things further.
Stipend/Living allowance: £14,000 per annum Start date 2nd October 2017 (exact start date may be negotiable)
Duration: 36 months
Data Scientist (KTP Associate – Fixed Term) Knowledge Transfer Partnerships
Salary: £25,000- £30,000 + depending on experience plus £4,000 development budget
Fixed term for approximately 2 years
Closing Date: TBC
Interview Date: Wednesday 27 July 2017 The post has been created as a result of an exciting and innovative Knowledge Transfer Partnership(https://www.gov.uk/guidance/knowledge-transfer-partnerships-what-they-are-and-how-to-apply) between Replay Maintenance Ltd, www.replaymaintenance.co.uk and the School of Computer Science in the College Science at the University of Lincoln (www.lincoln.ac.uk). Replay Maintenance Ltd operates across the UK providing maintenance packages to suit every type, age and condition of synthetic sports surface. The customer base includes private owners, Clubs, Schools, Universities, Leisure Centres, Prisons, MOD bases, major Facility Management companies. We are looking to recruit a Computer Scientist with a strong postgraduate or undergraduate (first class or upper second class) in Computer Science, IT for Business Computer Systems, Statistics and Machine Learning to undertake and manage this 24 month Knowledge Transfer Partnership (KTP). This project aims to develop a data mining platform to revolutionise synthetic sport surface maintenance practice and principles. Skills required: Proficient in software development, e.g. C#, Python, SQL. Knowledge and/or experience of data mining and machine learning Knowledge and/or experience of cloud-based databases, e.g., Microsoft Azure/AWS Excellent communication and team working skills The KTP Associate will be employed by the University of Lincoln for 2 years but be based at Replay Maintenance Ltd Head office in Long Bennington and subject to its employment practices and conditions of work. The successful candidate will be supervised both by Replay Maintenance Ltd and the College of Science at the University of Lincoln. The project will develop a data analytics platform to revolutionise synthetic sport surface maintenance practice and principles. Key project objectives are: To develop a novel bespoke software solution that will enable Replay Maintenance Ltd to enhance its service delivery and capture data that can be utilised in a unique offering. To complete a review of the current process and system, and understand the data challenges. To develop a cloud based database. To develop the state of the art data mining and machine learning algorithms To manage the collaboration between the University of Lincoln and Replay Maintenance Ltd. Commercial Context:
Replay operate in a highly competitive market. To expand their market share, they need to develop their business further and adopt and embed more innovation into their services and processes. Over the next few years there will be in excess of £250 million pounds spent on the build of new synthetic sports pitches and an anticipated annual spend on maintenance being in the region of £10-£15 million. Currently, there is however a lack of knowledge in the market relating to how surfaces perform, react and deteriorate. This project is therefore designed to develop an innovative way of utilising and enhancing data collected at assessment. This is not something that exists within the industry at present in the UK or worldwide so has great scalability. This innovation would be ground–breaking, giving Replay Maintenance a major advantage in the market and strengthening relationships with key clients. If you have any questions, please contact Dr. Bowei Chen (email@example.com) for more details.
Research Fellow in Machine Learning and Deep Neural Architectures for Signal Analysis and Prediction School of Computer Science, University of Lincoln, UK
Salary: From £32,004 per annum
This post is fixed term for 27 months
Closing Date: Thursday 08 June 2017
Interview Date: Monday 19 June 2017
Reference: COS417 We seek to employ a highly motivated post-doctoral researcher that will be working on the EU-H2020 project “CORe monitoring Techniques and EXperimental validation and demonstration (CORTEX)”. The project will give the opportunity to the post holder to engage with other partners of the project across Europe. You should hold a PhD (or equivalent) or be near to completion, and should be able to demonstrate a good track record in at least one of the following research fields: Machine Learning Deep Learning Signal Processing Applications Pattern Analysis The contract is fixed for 27 months (September 2017 – December 2019). This position is funded by the EU-H2020 project CORe monitoring Techniques and EXperimental validation and demonstration. The CORTEX project aims at developing core monitoring techniques that can be used to detect and characterize operational problems, before they have any inadvertent effect on plant safety and availability. The project relies on using the fluctuations existing in any process parameter that can be measured at the plants in order to monitor their state, primarily the neutron flux. The postholder will work on applying machine learning and deep learning techniques for analysing the collected signals and produce effective and transparent predictions. Once in post, you will be working with Professor Stefanos Kollias (Principal Investigator) and Dr Georgios Leontidis (Co-Investigator) on the aforementioned research topics. It is also expected to have a direct collaboration with other partners of the project. The successful candidate will be a member of the newly founded Machine Learning research group (Mlearn). The Mlearn is a rapidly expanding group, part of the School of Computer Science at the University of Lincoln and specialises in the development of machine learning, neural networks, deep learning techniques, decision-making systems, signal processing and big data and various application fields ranging from healthcare to financial applications. We provide a highly-dynamic inter-disciplinary research environment with a broad range of collaboration opportunities. In this project, you will have access to various sources and knowledge within the University, as well as provided by other partners of the project. The University of Lincoln is a forward-thinking, ambitious institution and you will be working in the heart of a thriving, beautiful, safe and friendly city. The School provides a stimulating environment for academic research, and is located on the picturesque waterfront campus in the historic and vibrant city of Lincoln. The University has just announced a £130M investment programme, a significant part of which is being invested in new, purpose-built facilities for the School of Computer Science. Lincoln itself is a small but fast growing city in the east-midlands. It offers a fantastic life quality given by moderate living costs, a medieval city centre including a famous cathedral and a beautiful ancient canal system that is still in use by many house boats nowadays. If you would like to know more about this opportunity, please contact Professor Stefanos Kollias (Founding Professor of Machine Learning, firstname.lastname@example.org).
Academic Positions Up to four posts have been publicised at lecturer and senior lecture level related to machine learning by the School of Computer Science of the University of Lincoln (closed now). Possible new posts will be announced in the future.