Postdoctoral Fellow - Mathematics & Statistics at University of Otago

Dunedin, Otago, New Zealand -

Full Time

Start Date

Immediate

Expiry Date

28 May, 26

Salary

92350.0

Posted On

27 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Bayesian Inversion, Machine Learning, Statistical Inference, Python, Julia, MATLAB, ICESat-2 Data, Wave Remote Sensing, Algorithm Development, Data Analysis, Scientific Programming, Inverse Problems, Geophysical Data, Surrogate Modelling, Validation

Industry

Research Services

Description

__________ DEPARTMENT OF MATHEMATICS AND STATISTICS | TE TARI PĀKARAU ME TE TATAURAKA 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. We are home to a strong community of researchers working across applied mathematics, geophysical fluid dynamics, and polar/environmental science, supported by excellent high-performance computing facilities. The Department of Mathematics and Statistics, in particular, is a vibrant research hub with strengths in mathematical modelling and computational methods for wave propagation phenomena. The role | Te mahi We invite applications for a Postdoctoral Fellow to join a world-leading international, interdisciplinary team on the project “The proof is in the pancake: connecting fundamental physics to data-driven detection of the emergent state of sea ice in the changing polar oceans”, funded through a Marsden grant. The core investigation team includes Dr Fabien Montiel (Project Director), Dr Alberto Alberello (University of East Anglia, UK), Dr Colin Whittaker (University of Auckland, NZ), Associate Professor Stuart Hawkins (Macquarie University, Australia), and Dr Joey Voermans (University of Melbourne, Australia), and is further strengthened by strategic partnerships with experts in wave remote sensing (Dr Alex Fraser, University of Tasmania, Australia). The project addresses a key limitation in current satellite monitoring: while sea-ice extent can be tracked, distinguishing between ice types at scale remains challenging. The team is developing a new pancake-ice detection method using recent breakthroughs in satellite wave observations, combining physics-based modelling and experiments with machine-learning–enhanced statistical inference. This position is based at the University of Otago in Dunedin and will be managed/supervised by Dr Fabien Montiel. The postdoctoral fellow will lead the “Inverse Problem” component of the project: developing and validating a satellite-based pancake-ice detection algorithm using ICESat-2 wave-damping observations within a Bayesian inversion framework, enhanced with machine learning. Key responsibilities will include: • Leading the design and implementation of the Bayesian inverse model for pancake-ice detection (in collaboration with project investigators). • Processing and analysing ICESat-2–derived wave attenuation (damping) data (2018–present) to support algorithm development and evaluation. • Integrating machine learning where appropriate (e.g., surrogate modelling to accelerate inference; complementary classification approaches) and conducting rigorous validation. • Delivering core outputs of the project: a validated detection algorithm and a pan-polar pancake-ice climatology for the Southern and Arctic Oceans. • Publishing results in high-quality journals and contributing to team manuscripts and project reporting. • Contributing to a supportive research culture, including collaboration across the international partner institutions. There will be an opportunity for successful candidate to assist with teaching for personal development, as agreed with their Principal Investigators and Heads of Department. Your skills and experience | Kā pūkeka me kā wheako We are seeking a highly motivated researcher who can drive a methodical programme of research and collaborate effectively across disciplines. Essential: • A completed PhD in a relevant field (e.g., applied mathematics, statistics, geophysics, remote sensing, physical oceanography, or related). • Strong scientific programming skills (e.g., Python/Julia/MATLAB) and experience working with large datasets. • Excellent communication skills and evidence of research outputs (publications, preprints, software, reports). Highly desirable: • Demonstrated experience in inverse problems and/or statistical inference (ideally Bayesian). • Prior experience with satellite remote sensing data and/or geophysical data pipelines (ICESat-2 experience a bonus). • Experience applying machine learning in scientific/engineering contexts (e.g., surrogate models, classifiers). • Familiarity with wave/sea-ice/cryosphere topics (helpful but not required). Further details | Pūroko This is a full-time (1.0 FTE), fixed-term position until March 2029 available from June 2026, based in Ōtepoti, Dunedin. The salary for this position is $92,350 per annum. 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 Dr Fabien Montiel, via the email address 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 2600295 will close on Wednesday, 1 April 2026. Applications will be reviewed as they are received. Additional Information Contact: Dr Fabien Montiel 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

Responsibilities

The postdoctoral fellow will lead the development and validation of a satellite-based pancake-ice detection algorithm using ICESat-2 wave-damping observations within a Bayesian inversion framework enhanced with machine learning. Key tasks include designing the inverse model, processing ICESat-2 data, integrating machine learning, and delivering a validated algorithm and a pan-polar pancake-ice climatology.