1. Job Details
Job title: Research Associate in Scientific Computing
School: School of Engineering
Unit: Institute for Digital Communications
Line manager: Dr. Nick Polydorides
2. Job Purpose
This Research Associate position will contribute to the development of randomised algebra methods for process analytics within the Institute for Digital Communications at the University of Edinburgh. The project is funded by EPSRC. To expedite the data processing within process analytics we suggest replacing the conventional deterministic computations with sample-based estimations involving only a few randomly chosen elements of the model matrices and data vectors. If this sampling is done optimally it could lead to a substantial saving in time and memory requirements, with only a minor degradation in the fidelity of the analytics. The job purpose is to deliver randomisation algorithms for two strategically selected case studies: the first one concerns large systems of linear equations derived by the implementation of the finite element method on the diffusion equation, and the second will be on the large-scale regression problem to estimate the diffusion coefficients. The two cases are well-coupled within the context of process analytics in that typically one would use a numerical scheme to predict the process’s response, while the combination of a finite element model with measured process data naturally leads to a regression inverse problem for diagnostics.
3. Main Responsibilities
Undertake research related to randomised linear algebra for real-time process analytics. This will entail both independent research work and working in collaboration with the PI and other researchers on randomised algebra for numerical computing. The successful candidate will proactively engage with collaborating researchers to identify fruitful lines of research in this area, with a view to understanding, defining, and quantifying the potential of randomised algebra in randomised algebra for solving partial differential equations and inverse problems. In particular, this involves the design of randomisation algorithms for finite element matrix sketching; design of subset selection algorithms in the context of large-scale regression; analysis of the randomisation-induced error to quantify the variance in the estimated quantities with respect to the optimality of the sampling distributions; and software implementation in Matlab or Python using serial or parallel platforms. (60% of time)
Dissemination of research findings through internal reports, conference proceedings, and journal publications (20% of time)
Liaison with and support of colleagues on the EPSRC funded RANDANA, possibly including domestic and international travel and support of meetings at the University of Edinburgh and occasionally to the Alan Turing Institute. (10% of time)
Develop plans for personal research and contribute to wider development of group strategy to identify and agree priorities for current and future work. (5% of time)
Contribute to writing bids to win ongoing research grants. Provide guidance to other staff and students interested in integrating methods/results from this work into their own activity to ensure most effective use, particularly within the University of Edinburgh (5% of time)
4. Planning and Organising
• Work with the line manager to define and plan a programme of relevant research work and then to produce publishable results within appropriate timescales
• Work independently to plan the schedule of tasks to ensure that work of the projects progresses according to agreed overall timetable
• Organise own time so that different aspects of the project can proceed in parallel giving due consideration to the needs of other members of the team
• Be proactive in interacting with project partners to share data and ideas
• Take a leadership role in regular project meetings to report and review progress, and to generate new ideas and lines of research
• Determine the aims, objectives and deadlines for short to medium term work plan, in discussion with the Line Manager (particularly for medium term planning).
• Contribute to long-term strategic planning and development of group activities, in discussion with the Line Manager and other researchers.
5. Problem Solving
• Be resourceful in overcoming problems encountered in the development of new approaches and the implementation of existing techniques in the normal course of the research.
• Define the methodology to solve the problems in the project.
• Work in close cooperation with the other project investigators, project partners, technical staff, and PhD students working on the project to resolve problems, as required.
6. Decision Making
• The post-holder should be able to organise his/her work schedule to ensure satisfactory progress and to meet the demands (where realistic) of other project partners, PhD students, etc. The successful candidate will play an important role as a member of the team in making daily decisions about their own plan of work and the allocation of time and resources.
• Manage day-to-day work load, making independent decisions about immediate priorities to meet short and medium term targets that have been agreed with Line Manager.
7. Key Contacts/Relationships
Frequent (daily to weekly) contact with other researchers in the project team and relevant research groups including academic staff, other researchers and PhD students.
Regular (weekly to monthly) contact with other researchers working on relevant problems in the University of Edinburgh.
Regular (typically monthly) contact with key stakeholders, likely to include other colleagues in Edinburgh, Oxford and Warwick.
8. Knowledge, Skills and Experience Needed for the Job
• A good undergraduate degree and a PhD in Engineering, Computer Science or Applied Mathematics, with a focus on numerical solution of partial differential equations and inverse problems, or a closely related area
• Proven track record of research in numerical computing, with a specific focus on finite element method or a closely related area.
• Experience in delivering research project results in a data science context; i.e. a record of peer-reviewed journal and conference papers in a relevant area.
• Capable of working collaboratively with researchers from different disciplines or technical background.
• Capable of working independently, exercising a high degree of initiative and demonstrating a pro-active and flexible approach to work.
• Ability to work under pressure and meet agreed milestones.
• Ability to develop and maintain effective working relationships.
• Good time-keeping and time-management, with associated effective prioritisation of tasks.
• Good communication skills; oral, writing and presentation of data.
• Ability to contribute ideas and initiate new ways of working.
• Ability to be adaptive and accepting of new ideas and a willingness to approach new challenges and adjusts plans to meet new priorities.
• Previous experience in GPU programming or parallel computing.
• Potential to help supervise a PhD student and 1-2 Masters project students
• Work collaboratively with other research associates, industrial and academic partners, PhD students from the participating and related groups and researchers from other universities
• Responsibility for regular progress reporting to project partners
• Keep line manager up-to-date with progress
• Contribute to interactions between immediate group and broader communities working in the fields of data science.