Are you passionate about probabilistic machine learning (ML), scientific datasets, and clean performant code? Would you like to feed your passion for science on cutting-edge research, from archaeology to particle physics, and share your experiences in workshops, blog posts, and talks? At the MLColab (Machine Learning ⇌ Science Colaboratory) of the University of Tübingen, we are looking for a motivated, skilled individual working at the intersection of science, engineering, and people.
We want to raise the power of scientific discovery by thoughtful application of machine learning techniques — closely working together with ML methodologists and University of Tübingen researchers in the natural sciences, social sciences, and humanities. Problems range from modeling the past climate using fossilized pollen data, to analyzing nuclear decays in large particle detectors for fundamental physics, to reconstructing oral transmission throughout the centuries from preserved ancient texts.
We tackle this challenge from several angles:
- we develop, implement, and deploy probabilistic models.
- we train and advise domain scientists on the use of ML, from feature selection to model evaluation.
- we assess best practices in scientific machine learning and share our progress with the community in both conventional and interactive formats.
- finally, we distill recent literature into open-source machine learning code to facilitate realistic and unbiased algorithm benchmarking and to empower researchers across disciplines.
For more details and to apply, see https://uni-tuebingen.de/universitaet/stellenangebote/newsfullview-stellenangebote/article/researcher-in-scientific-ml-m-f-d-e13-tv-l-100/