We are seeking a highly motivated candidate to join our research team and contribute to the development of novel statistical machine learning methodologies, with a particular focus on deep Gaussian processes and transfer learning for battery data within a Bayesian framework. The project will investigate advanced probabilistic modelling approaches and their application to challenges in battery technology, including the potential use of deep Gaussian processes for capturing complex nonlinear relationships and uncertainty.
The programme is conducted in partnership with Analog Devices, Inc. (NASDAQ: ADI). Close collaboration with ADI researchers will form a key component of the project, providing the successful candidate with valuable exposure to the translation of cutting-edge research into industrial applications. Subject to visa requirements and funding availability, the position is also expected to include a research placement at the Analog Garage Research Lab in Boston, Massachusetts, USA.
The PhD position is fully funded for three years and includes payment of university fees together with a tax-free stipend of up to €25,000 per annum. Additional remuneration opportunities may be available through tutoring and grading activities. Funding is also provided for computing equipment, conference travel, and other research-related expenses.
The successful candidate will join the Department's lively, supportive, and growing research community, which currently includes approximately 12 PhD students.
Application: Applicants should apply using the form below.
Applications submitted by email will not be considered.
An application should include a 2-page CV and a short cover letter (2-pages max) indicating how the applicant's skills align with the research project and their motivation for applying. The application CV should, at minimum, include the applicant's name, educational institution, qualification stating overall grade/percentage (predicted grades are acceptable for those still studying) and contact details of two academic referees. Supporting documentation such as official transcripts (where relevant) are also required.
Informal queries can be made to: james.a.sweeney@ul.ie. Please include "PhD Query" followed by your name in the subject line.
To apply: please apply here.
Funding Notes
Project is funded in conjunction with Analog Devices Inc.
