Le 20/06/2019 par Marcel Mongeau :

A Postdoctoral Research Associate position is available at the University of Edinburgh with a focus on the intersection of mathematical optimization and machine learning with applications to energy and transportation. Based in Edinburgh, the position comes with the opportunity for extended visits to project collaborators in France and Canada. The post-holder will be encouraged to pursue his or her own, and newly arising, research questions, and will also be expected to contribute modestly to teaching in a manner compatible with his/her research expertise. Preference may be given to candidates with proven expertise in one or more of the following: - mixed-integer linear or nonlinear optimization; - machine learning; - modeling and optimization of energy and/or revenue management. Candidates should have (or soon to obtain) a PhD in mathematical optimization, operational research, or a related field, by the time of the start of the position and have expertise in at least one of the areas of mixed-integer linear or nonlinear optimization, machine learning, modeling and optimization of energy and/or revenue management. They should also have strong coding skills (one or more of Java, C++, Julia) and experience using one or more commercial optimization solvers. Candidates should be enthusiastic about learning new methods, and applying their expertise in innovative ways. Prior experience in interdisciplinary research and/or in providing efficient solutions to problems arising from practical/industrial applications will be an advantage. The post is available from 1st September 2019, or a mutually agreed date, for a 24-month fixed term contract. The closing date for receipt of applications, including letters of reference, Friday 28 June 2019 at 5pm (GMT). More information and the application procedure can be found at: https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=048107 Informal enquiries are encouraged; please contact Professor Miguel Anjos by email: Miguel.F.Anjos@ed.ac.uk