Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
focus areas. We are looking for curious minds who are excited to push the boundaries of responsible AI. Learn more about the lab's work at: https://martinpawelczyk.github.io/ . Tasks and Responsibilities
-
are excited to push the boundaries of responsible AI. Learn more about the lab's work at: https://martinpawelczyk.github.io/ . Tasks and Responsibilities Develop machine learning methods and tools with a
-
, Machine Learning, Artificial Intelligence, Mathematics, or similar (if the Master is not yet completed, please submit a short statement by the supervisor concerning progress) excellent coding skills (Python
-
; Experience with programming (e.g. Python) is a plus; Familiarity with membrane biophysics or soft matter physics is advantageous; Strong written and oral communication skills in English; Ability to work
-
of using Python or R Very good written and spoken English (min. B2) In addition, the following criteria are desirable: basic knowledge of analytical chemistry, neurobiology, or behavioural ecology Experience
-
in R and/or Python, and/or experience analyzing sequencing data in a bioinformatics context Preferred Prior work with single-cell RNA-seq (QC, normalization/integration, differential expression
-
, bioinformatics, computational biology, statistics, computer science, or similar Solid programming in R and/or Python, and/or experience analyzing sequencing data in a bioinformatics context Preferred Prior work
-
(preferably in R, Python, GIS) • Competences in quantitative research methods - ideally knowledge of several of the following aspects of quantitative data analysis: analysis of large/longitudinal datasets
-
-authored peer reviewed papers) • Well-developed statistical software skills (preferably in R, Python, GIS) • Competences in quantitative research methods – ideally knowledge of several of the following
-
. This position will be part of the Environmental Psychology research group (see https://env-psy.univie.ac.at/about-us/) and will be supervised by Profs Sabine Pahl and Alexandra Brausmann (environmental economics