·Be familiar with Matlab, and Python,
·Experience in solving inverse problems using statistical modelling (such as Bayesian inference), and different algorithms (such as optimization, stochastic and hybrid plug-and-play methods)
·Experience of working in a Higher Education or similar environment.
·Applicants should hold a PhD in a relevant area of Physics, Electrical Engineering, or Mathematics (or a thesis submitted by the start date of the project).
·A record of high quality publications, and evidence of contribution to the writing of these publications proportionate to opportunity.
·Appropriate publication record for stage in career.
·Must have proven academic ability and a demonstrable high level of technical competence in the analysis / modelling of the results and in developing suitable computational methods.
·Strong theoretical understanding and thorough grounding in methods relevant to the project.
·Ability to articulate research work, both in technical reports / papers and by oral presentation.
·Ability to formulate and progress work on their own initiative.
·Evidence of research ability: problem solving, flexibility.
·Must be able to work as part of a team on the experiments at Heriot-Watt and more widely with the collaborators at other Universities. An ability to travel widely is desirable.
·Experience in writing and managing peer-reviewed papers.
·Experience in public presentations of scientific results (e.g. conference talks).
·Energy and enthusiasm for the project.
·Be familiar with C and parallel programing tools (GPU)
·Evidence of ability to present work effectively in person, e.g. at conferences and seminars.
·Previous experience with one or more of the following experimental areas: Computational imaging; Machine Learning; Statistical Signal Processing; quantum enhanced imaging.
·Experience in leading the writing of scientific papers.
·Evidence of securing own funding.
·Evidence of ability, subject to opportunity, to guide other researchers, e.g. PhD students and undergraduate project students.
·Experience of research student supervision.
·Capability to be self-directed and think innovatively.