At TU Delft, within the statistics group there are vacancies for two PhD
students and one postdoc.
PhD student in computational statistics (contact Joris Bierkens or Frank
van der Meulen)
https://vacature.beta.tudelft.nl/vacaturesite/permalink/45564/?lang=en
Diffusion processes are natural statistical models for many phenomena,
such as modelling financial time series and dynamics of biological
systems. In practice, available data are obtained discretely in time
and usually incomplete, in the sense that some function of the state of
the diffusion is observed. The statistical analysis of such data poses a
formidable problem. Over the past decades there has been much research
on this problem and progress has been made in developing Bayesian
computational methods, mainly using Markov Chain Monte Carlo (MCMC)
methods. Unfortunately, these methods are computationally demanding and
in their present form only allow for estimation in diffusion models of
low dimension. Many applications however utilise models of high dimension.
The candidate possesses an MSc degree in mathematics (specialisation
statistics or probability theory) or a related discipline such as
physics, computer science or econometrics (with strong emphasis on
mathematics, statistics and/or machine learning). (S)he must be highly
motivated and ambitious with a strong curriculum in statistics and
stochastic processes. As this project falls within computational
statistics and stochastic simulation, good programming skills are highly
relevant. In addition, we require very good communication skills and
fluently spoken and written English.
Deadline: 1 May 2018
PhD students in data science for injury prevention in sports, see II)
PhD position (contact Frank van der Meulen or Jakob Soehl)
https://www.academictransfer.com/en/46611/phd-human-movement-sciences-2-phds-reduction-of-injuries-related-with-strength-training/
The candidate possesses an MSc degree in mathematics (specialization
statistics or probability theory) or a related discipline such as
physics, computer science or econometrics (with strong emphasis on
mathematics, statistics and/or machine learning). Some experience with
epidemiology, statistical modelling and handling / analysis of data sets
is advantageous. The candidate must be highly motivated and ambitious,
have a strong curriculum in statistical methodology and data modelling
and a keen interest in sports engineering or injury prevention
applications. The position is jointly at Delft Institute of Applied
Mathematics and the Department Biomechanical Engineering.
Deadline: 1 May 2018
Postdoc on probabilistic extreme weather forecasting (contact Juan-Juan
Cai)
https://vacature.beta.tudelft.nl/vacaturesite/permalink/46531/?lang=en
There is an urgent need of statistical sound prediction methods for
extreme weather, which often has a strong disruptive societal impact.
The research project “Probabilistic forecasts of extreme weather
utilizing advanced methods from extreme value theory” is funded by
NWO-TTW (the former STW) and is a collaboration between the statistics
section, Delft University of Technology (TU Delft) and the R&D Weather
and Climate Modelling department of the Royal Netherlands Meteorological
Institute (KNMI).
Applicants should have the following qualifications:
PhD on statistical and/or machine learning methods including application
of these methods to (big) data, preferably in a probabilistic (weather)
forecasting framework;
preferably knowledge of extreme value theory and/or statistical
post-processing and verification methods;
highly motivated and interested in meteorology;
experienced in (statistical) programming, preferably in R;
very good communication skills and fluent spoken and written English.
Deadline: 10 May 2018
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