哥德堡大学应用数学和统计系招收博士研究生
Doctoral positionin Applied Mathematics and Statistics
At the division of Applied Mathematics and Statistics we conduct research within the theory and implementation of finite element methods, inverse wave propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large-scale computational methods, and bioinformatics. The division is also expanding in the area of data science and machine learning.
Our department continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all our activities. We work actively to be a parent-friendly organization.
We are looking for a motivated, independent student with strong computational skills and a broad interest in mathematics, statistics and biology. The project is part of a large research consortium into the modeling of invasive cancer growth. The PhD student will be working closely with systems biology and computational biology research teams at Uppsala and Stockholm University as well as the computational statistics group at Chalmers/GU. The primary research topic is the development of new matrix- and tensor-imputation methods and advanced machine learning strategies for accelerated drug-discovery. The data resources are state-of-the art spatial transcriptomics and CRISPR perturb-seq single cell data and you will be expected to work closely with the teams that generate these data sets in order to ensure that methods are customized to the explicit data challenges at hand.
Application procedure
Submit the following documents:
Application letter (max 2 pages) where you introduce yourself and describe your background and research interests
Detailed curriculum vitae
Electronic copies of your master and bachelor transcripts
Copies of your bachelor’s and master’s thesis if available
Names of at least two reference persons
Contact information
Rebecka Jörnsten, Professor, Project supervisor
+46 760 491949, jornsten@chalmers.se
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