Post-Doctoral Research Associate in Machine Learning for Autonomous Alignment
Post-Doctoral Research Associate in Machine Learning for Autonomous Alignment
Heriot-Watt University - School of Engineering & Physical Sciences
Edinburgh, United Kingdom
Detailed Description
The successful applicant will develop and research Machine Learning (ML) systems for the optimisation Complex Optical Systems.
This post is part of a major project that is focused on automating such alignment processes using either, or both, robotic or mechatronic alignment.
It is expected that the successful applicant will have experience in machine learning, automation and/or optimisation systems and will be able to rapidly embrace the breadth of knowledge and technical challenges of designing and developing a complete autonomous manufacturing capability for laser systems.
Research Environment
They will be expected to work directly with, Dr Richard Carter, Prof M J Daniel Esser, Prof Mike Chantler, the Leonardo engineers involved in the project, as well as Prof Jonathan Corney from University of Edinburgh. The applicant will also work with PhD students and the other PDRAs of the Prosperity Partnership.
The PDRA will report on project progress and outcomes to the Prosperity Partnership Management Group, as well as participating in Knowledge Transfer Meetings and Workshops with a broad range of Leonardo personnel. Most of the project will be executed at the National Robotarium at Heriot-Watt University in Edinburgh, with some joint work also executed on the manufacturing site at Leonardo UK, Edinburgh and the University of Edinburgh.
Key Duties and Responsibilities
- We are looking for a creative and highly motivated researcher willing to work as part of a team. Good communication skills and an appropriate publication record are essential;
- General tasks will involve scientific research; analysis and interpretation of data; daily oversight of the activities of postgraduate and undergraduate project students in the laboratory; communication with other investigators involved in this collaborative project; preparation of scientific papers; presentation of research at partnership workshops and meetings, as well as national and international conferences, whilst also supervising the activities of junior group members and PhD students;
- The successful candidate will be expected to contribute to experimental and theoretical design and procedure as part in a collaborative decision-making process, while taking responsibility for implementing experiments, theoretical models, and data analysis;
- Responsibilities will also include assistance in the day-to-day maintenance of the experimental facilities, liaising with external collaborators, assisting in the development of student research skills, and contributing to teaching (lab and tutorial demonstrations) in relevant taught courses within Physics and Engineering;
- The successful candidate is also expected to be involved in our outreach activities, with roles that can be tuned to the specific preferences of the candidate but will involve for example interviews, talks for the public and preparation of experimental demonstrators for use in schools.
Education, Qualifications & Experience
Essential Criteria:
- Applicants should hold a PhD in a relevant area of Physics, Computer Science, or related subject (or a thesis submitted by the start date of the position);
- Ability to articulate research work via written technical reports, academic publishing and by oral presentation;
- Must have proven academic ability and a demonstrable high level of technical competence in machine learning and/or optimisation systems;
- Theoretical or experimental experience in an area of direct relevance to the project (i.e. machine learning, systems optimisation);
- Ability to formulate and progress work on their own initiative with evidence of research ability: problem solving, flexibility;
- Must be able to work as part of a team on the experiments at Heriot-Watt in Edinburgh out with the specific project and more widely with the collaborators of the project; This appointment is in partnership with Leonardo and is conditional upon you being able to demonstrate eligibility for UK Security Clearance.
Desirable Criteria:
- Experience working as part of a highly interdisciplinary team;
- Experience of experimental design and integration;
- Experience of Python;
- Understanding and experience in one or more additional areas relevant to the project:
- Robotic/Mechatronic systems: design, programming, construction;
- Laser system design: optical, mechanical, and thermal design applied to industrial/defence laser systems;
- Experience in implementing manufacturing automation;
- Experience in translating research to an industrial environment;
- Experience of CAD software.
- Experience of programming for data acquisition (e.g. LabVIEW, Arduino), analysis (Mathematica, Matlab) and/or modelling (Comsol, Matlab, Zemax etc.);
- Evidence of ability, subject to opportunity, to guide other researchers, e.g. PhD students and undergraduate project students;
- Ability and willingness to learn new digital skills and capabilities appropriate to your role and how it evolves;
- Understand and adhere to information security practices particularly as it relates to personal and commercially sensitive information;
- Energy and enthusiasm for the project.
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