Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
research in plant physiology, experience with sensor networks (automated dendrometers, sap-flow systems) and with processing and statistical analyses of large datasets is important. Teamwork within the group
-
extremes under different air pollution along a climatic gradient in Europe. Moreover, we will use statistical (e.g., random forest, generalised additive models) and process-based (e.g., SPA) models
-
, econometrics, management/ decision sciences mathematics, statistics, computer science, physics or related fields. Your research track is consistent and shows a track record, or clear potential, for modelling
-
, programming and statistical evaluation Machine learning analyses: implementation of established and new workflows Coordination of activities with Consortium partners, including presentation of results
-
which work together to combine cutting edge statistical and computational tools with quantitative experiments. A list of our group's publications can be found on Google Scholar . [1] 1. M. Kaiser, et al
-
it includes both experimental and theoretical researchers which work together to combine cutting edge statistical and computational tools with quantitative experiments. A list of our group's
-
credible transition pathways to Swiss society and politics. Main duties and responsibilities include : Your task will be to develop a statistical methodology which makes best use of high-resolution future
-
of Agricultural Sciences at ETH Zurich investigates animal (epi-)genomes and transcriptomes. The group applies bioinformatics and statistical methods to detect and characterize trait-associated sequence variation
-
and statistical methods to detect and characterize trait-associated sequence variation from short and long-read sequencing data. We are looking for a motivated researcher who is eager to work on cutting
-
experience in Machine Learning with a PhD degree from a university in Computer Science, or related fields, with a proven track record in statistical machine learning, deep learning, and graphical modelling