Postdoctoral Fellow - Physics

Beginning of the main content section. Back to prior page Printable Format Job 1 out of 24 Previous | 1 2 3 4 5 | Next Job Description - Postdoctoral Fellow - Physics (2600856) Postdoctoral Fellow - Physics - 2600856 __________ DEPARTMENT OF PHYSICS | TE TARI HŪ-O-TE-KŌHAO DIVISION OF SCIENCES | TE ROHE A AHIKĀROA Who we are | Ko wai mātou The University of Otago, established in 1869, has a long and distinguished tradition of teaching and research. The Department of Physics hosts internationally recognised research programmes spanning astrophysics, condensed matter, geophysics, medical physics, and computational science. The successful applicant will join the Inference Group, led by Associate Professor Colin Fox, which develops new mathematical and computational methods for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi We invite applications for a Postdoctoral Fellow to join an international research team on the Marsden Fund project Scalable Bayesian Algorithms for Multi-Physics Inverse Problems with High-Level Representations. The project is led by Associate Professor Colin Fox (University of Otago) and brings together leading researchers in Bayesian computation and inverse problems, including Dr Tiangang Cui (University of Sydney), Prof. habil. Oliver Ernst (TU Chemnitz, Germany), and Dr Youssef Marzouk (Massachusetts Institute of Technology, USA). The project aims to develop the next generation of Bayesian algorithms for large-scale inverse problems by combining interpretable high-level probabilistic models, multi-physics data integration, and modern machine learning. The resulting methods will be validated on groundwater imaging problems in New Zealand, where indirect geophysical measurements provide a critical source of information about hidden subsurface structure. This position is based at the University of Otago in Dunedin and will be supervised by Associate Professor Colin Fox. The successful applicant will play a leading role in developing scalable Bayesian algorithms, probabilistic models, and computational methods for high-dimensional inference, working closely with an internationally recognised team of collaborators. Key responsibilities will include: •    Develop scalable Bayesian inference algorithms for inverse problems with high-level structural representations. •    Develop high-level probabilistic models of subsurface geological structure. •    Develop methods for integrating multiple geophysical data types within a unified Bayesian inference framework. •    Develop rapid Bayesian inversion methods to support adaptive field acquisition. •    Integrate modern machine learning techniques to accelerate Bayesian computation, including surrogate models, efficient proposal mechanisms, and learned transport maps. •    Analyse the mathematical properties of proposed algorithms, including convergence and ergodicity where appropriate. •    Deliver computational methods and open-source software for groundwater imaging in representative New Zealand settings. •    Publish research in high-impact journals and present results at leading international conferences. •    Mentor postgraduate students and contribute to the supervision of research students. •    Collaborate effectively with members of the Marsden research team and external project partners. •    Contribute to the development of an internationally recognised research programme in scalable Bayesian computation. The successful candidate will have the opportunity to contribute to teaching for professional development, as agreed with the Principal Investigators and Head of Department. Your skills and experience/Kā pūkeka me kā wheako We are seeking a highly motivated researcher who can develop an independent programme of research and collaborate effectively across disciplines. Essential •    A PhD in applied mathematics, statistics, computer science, computational physics, geophysics, or a closely related discipline. •    Demonstrated research ability in Bayesian computation, inverse problems, uncertainty quantification, machine learning, scientific computing, or computational statistics. •    Excellent programming skills (Python preferred; Julia, MATLAB or C++ also welcome). •    Strong written and verbal communication skills. •    Evidence of high-quality research publications or equivalent research outputs. Highly desirable •    Experience with Bayesian computation (MCMC, SMC, variational inference, transport methods, etc.). •    Experience with inverse problems or uncertainty quantification. •    Experience with scientific machine learning. •    Experience with high-performance computing. •    Experience with geophysical imaging or environmental applications. Further details/Pūroko This is a full-time (1.0 FTE), fixed-term position for 2 years from December 2026, based in Dunedin. The start date may be negotiable with the ideal candidate. The salary for this position is $92,350 per annum. The position includes opportunities to collaborate internationally with project investigators in Australia, Germany and the United States, and to present research at leading international conferences. The Dunedin campus is consistently ranked among the most beautiful in the world. The city offers affordable living, a safe environment, and outstanding outdoor recreation within easy reach. The University aspires to be a Te Tiriti led university. The foundation of this is our relationship with mana whenua as described in Te Kaha Uia Te Kaha, the Mana-to-Mana Agreement. We are committed to working closely with iwi and Māori organisations as matauranga Māori becomes an integral part of teaching and research across the institution. For further information, please contact Associate Professor Colin Fox, via the contact details below. Application/Tono Candidates are requested to submit: •    Cover letter, including your interest in and fit for the inverse-problem focus of the project •    Curriculum Vitae, including a list of publications •    Names and contact details of three referees To submit your application, please click the apply button. Applications quoting reference number 2600856 will close on Tuesday, 1 September 2026 11.59PM (NZST). Additional Information Contact: Associate Professor Colin Fox Position details: Information Statement Guidelines for Academic Positions: Guidelines Further Information: Department Website Create an email with a link to this vacancy: Create email Location: About Dunedin Primary Location NZL-SI-Dunedin Employment Status Fixed Term Salary Level and Range Postdoctoral Fellow ($92,350) Organisation Physics Job Function Research Job 1 out of 24 Previous | 1 2 3 4 5 | Next

• We invite applications for a Postdoctoral Fellow to join an international research team on the Marsden Fund project Scalable Bayesian Algorithms for Multi-Physics Inverse Problems with High-Level Representations.
• The project is led by Associate Professor Colin Fox (University of Otago) and brings together leading researchers in Bayesian computation and inverse problems, including Dr Tiangang Cui (University of Sydney), Prof. habil.
• Oliver Ernst (TU Chemnitz, Germany), and Dr Youssef Marzouk (Massachusetts Institute of Technology, USA).
• The project aims to develop the next generation of Bayesian algorithms for large-scale inverse problems by combining interpretable high-level probabilistic models, multi-physics data integration, and modern machine learning.
• The resulting methods will be validated on groundwater imaging problems in New Zealand, where indirect geophysical measurements provide a critical source of information about hidden subsurface structure.
• This position is based at the University of Otago in Dunedin and will be supervised by Associate Professor Colin Fox.
• The successful applicant will play a leading role in developing scalable Bayesian algorithms, probabilistic models, and computational methods for high-dimensional inference, working closely with an internationally recognised team of collaborators.
• Develop scalable Bayesian inference algorithms for inverse problems with high-level structural representations.
• Develop high-level probabilistic models of subsurface geological structure.
• Develop methods for integrating multiple geophysical data types within a unified Bayesian inference framework.
• Develop rapid Bayesian inversion methods to support adaptive field acquisition.
• Integrate modern machine learning techniques to accelerate Bayesian computation, including surrogate models, efficient proposal mechanisms, and learned transport maps.
• Analyse the mathematical properties of proposed algorithms, including convergence and ergodicity where appropriate.
• Deliver computational methods and open-source software for groundwater imaging in representative New Zealand settings.
• Publish research in high-impact journals and present results at leading international conferences.
• Mentor postgraduate students and contribute to the supervision of research students.
• Collaborate effectively with members of the Marsden research team and external project partners.
• Contribute to the development of an internationally recognised research programme in scalable Bayesian computation.
• The successful candidate will have the opportunity to contribute to teaching for professional development, as agreed with the Principal Investigators and Head of Department.

• We are seeking a highly motivated researcher who can develop an independent programme of research and collaborate effectively across disciplines.
• A PhD in applied mathematics, statistics, computer science, computational physics, geophysics, or a closely related discipline.
• Demonstrated research ability in Bayesian computation, inverse problems, uncertainty quantification, machine learning, scientific computing, or computational statistics.
• Excellent programming skills (Python preferred
• Julia, MATLAB or C++ also welcome).
• Strong written and verbal communication skills.
• Evidence of high-quality research publications or equivalent research outputs.
• Experience with Bayesian computation (MCMC, SMC, variational inference, transport methods, etc.).
• Experience with inverse problems or uncertainty quantification.
• Experience with scientific machine learning.
• Experience with high-performance computing.
• Experience with geophysical imaging or environmental applications.