[ABE-L] Fw: PhD Scholarships in Statistics, University of Auckland

['Jorge Luis Bazan' via abe-l@ime.usp.br]

Divulgo informação  de 2 bolsas de doutorado na University of Aucklanda pedido do professor Jose Romeo    

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From: Renate Meyer <renate.meyer em auckland.ac.nz>
Date: Thu, 3 Mar 2022 at 16:35
Subject: PhD Scholarships in Statistics, University of Auckland

Two PhD scholarships are available in the Department of Statistics at the University of Auckland within the NZ Gravity Working Group (https://www.gravity.ac.nz) to work on statistical data analysis for the Laser Interferometer Space Antenna (LISA) mission (https://www.cosmos.esa.int/web/LISA). The cross-institutional research team spans statistics, mathematics, astrophysics and cosmology with members at five New Zealand universities as well as international collaborators within the International LISA Consortium (https://www.elisascience.org/articles/lisa-consortium).
We are seeking highly motivated and skilled students in statistics, mathematics, physics or related discipline, with a strong background in one or more of the areas of Bayesian theory and data analysis, Monte Carlo methods, time series, machine learning methods, astrophysics with sound computing skills and a keen interest in interdisciplinary research in gravitational wave science. Knowledge of and experience with Bayesian nonparametric techniques will be an added advantage. More details regarding each PhD research project are given below.
The PhD scholarships will be available from April 2022 and provide an annual (tax-free) stipend of NZD 35,000 plus tuition fees for three years. Further financial support can be obtained through teaching assistant work and part-time temporary tutoring. Starting dates are flexible throughout the year. Please send your cover letter, CV, transcripts, and the names and email addresses of two referees to Professor Renate Meyer (Renate.Meyer em auckland.ac.nz<mailto:Renate.Meyer em auckland.ac.nz>) and indicate the date you will be able to start.

Please note that all New Zealand universities currently require students and staff to be vaccinated against Covid-19.

PhD research project 1: Bayesian strategies for noise modelling and estimation of the stochastic gravitational wave background with LISA The research project will investigate novel statistical strategies for parameter estimation of gravitational wave signals observed by the Laser Interferometer Space Antenna (LISA). One aim is to estimate the power spectrum of the stochastic gravitational wave background produced by the emission from all galactic binaries and separate this from the astrophysical and primordial backgrounds. Another aim is to develop novel Bayesian methods for mitigating the influence of detector and background noise on the estimation of gravitational wave signals observed by LISA. The focus will be on robust nonparametric Bayesian methods for estimating the spectral density of  time series. These should take the idiosyncrasies of LISA measurements into account such as correlations between the channels, non-stationarities induced by LISA's orbital motion for long-lived signals, glitches and data gaps.

PhD research project 2: Bayesian tools for gravitational wave data analysis Extreme Mass-Ratio Inspirals (EMRIs) are among the most fascinating LISA sources but the signal templates are challenging to compute with the necessary accuracy. A typical EMRI signal will be observed by LISA for about 10,000 orbits making it especially sensitive to waveform inaccuracies. Consequently, Bayesian inference for features of interest is often computationally expensive and subject to model misspecification. This project focuses on statistical computing in Bayesian inference with gravitational wave applications using state-of-the-art statistical methods and machine learning techniques. The goal is to develop novel statistical computing methods to deal with challenges in Bayesian inference with complex models, overcoming current limitations of traditional techniques. The focus will be on Bayesian calibration for posterior approximation, robust inference, and deep learning approaches.

  
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