Postdoctoral Research Associates - Data-Driven Windfarm Modelling & Control

Organisation/Company UNIVERSITY OF SYDNEY Research Field Computer science Engineering Mathematics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Australia Application Deadline 10 Jun 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? NoOffer DescriptionFull time fixed term for 2 yearsTwo positions available to join one of the world's largest robotics research institutesBase Salary $105,314 - $116,679 + 17% superannuationAbout the opportunityWe are seeking two Postdoctoral Research Associates to join the Australian Centre for Robotics and the Net Zero Institute at the University of Sydney, Australia. The successful applicants will work at Sydney with Prof. Ian Manchester and Prof Gregor Verbic, in collaboration with Pro.f Ben Thornber (Queens University Belfast, UK) and Prof. Dr Harald K stler (Friedrich Alexander University of Erlangen, Germany) on a project funded in part by the Australian Research Council.The successful applicants will work with this team to develop new approaches to optimisation and control of large-scale windfarms in low-carbon electric power systems, taking into account wake interactions between individual wind turbines. The project focus is on how to generate and utilize reduced-complexity predictive models for windfarm control, from combinations of computational fluid dynamics and experimental data.You will join the Australian Centre for Robotics (ACFR), at the University of Sydney. The ACFR is one of the largest robotics research institutes in the world, with over 140 members including faculty, research fellows, technical staff, and postgraduate students. The ACFR performs fundamental research on perception, control, modelling, learning, and systems engineering and has strong industry and scientific collaborations in sectors including mining, aviation, agriculture, manufacturing defence, and environmental resilience. You will also be associated with the University of Sydney's newly-established Net Zero Institute, a university-wide multi-disciplinary initiative researching technologies and systems that address climate change risk, carbon removal, and emissions avoidance both through zero emissions energy and demand reduction.About youThe University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance of recruiting talent aligned to these values and are looking for Postdoctoral Research Associates.Position 1 - Physics-informed learning and control of windfarmsFor this position the research activity is expected to develop new methods in robust and physics-informed machine learning, reduced-order modelling, data-driven nonlinear model predictive control, and/or reinforcement learning.PhD (or soon to be completed) in engineering, applied mathematics, or computer science, with an ability to start in mid-late 2025a research track record, potential or demonstrated, of international standingstrong mathematical and computational skills, and experience with machine learning tools and software libraries, as well as dynamical system simulation and controldemonstrated expertise and research experience in machine learning (especially robust, control-oriented, or physics-informed machine learning), model predictive control, reduced-order modelling, wind power system dynamics, control of fluid systems, or a related fieldexcellent written and verbal English communication skillsdemonstrated ability to work as a member of a team and independently.experience with learning-based reduced-order modelling of complex systems such as fluid dynamicsexperience with power system dynamics and optimisation.For this position the research activity will apply and advance on fast CFD methods for full wind farm simulation based on Large-Eddy-Simulation to generate data which can be coupled with robust and physics-informed machine learning and reduced-order modelling methods for model predictive control and reinforcement learning.PhD (or soon to be completed) in unsteady large eddy simulation or detached eddy simulation coupled with reduced order rotor blade modelling for systems with rotating wings, with an ability to start in mid-late 2025a research track record, potential or demonstrated, of international standingstrong mathematical and computational skills, and experience with open source CFD software packages such as OpenFOAM or WaLBerla.demonstrated expertise and research experience in large-eddy simulation combined with actuator models for rotating wings, wind turbines and/or complete wind farm modellingexcellent written and verbal English communication skillsdemonstrated ability to work as a member of a team and independently.experience developing and coupling multiple physics models together in an open-source software package.Your employment is conditional upon the completion of all role required pre-employment or background checks in terms satisfactory to the University. Similarly, your ongoing employment is conditional upon the satisfactory maintenance of all relevant clearances and background check requirements. If you do not meet these conditions, the University may take any necessary step, including the termination of your employment.Applications (including a cover letter, CV, and any additional supporting documentation) can be submitted via the Apply button at the top of the page.For employees of the University or contingent workers, please login into your Workday account and navigate to the Career icon on your Dashboard. Click on USYD Find Jobs and apply.For a confidential discussion about the role, or if you require reasonable adjustment or any documents in alternate formats, please contact Cherie Goodwin or Rebecca Astar, Recruitment Operations by email to The University of SydneyThe University reserves the right not to proceed with any appointment. #J-18808-Ljbffr