[Position] Researcher in scientific ML - MLColab (Machine Learning ⇌ Science Colaboratory) of the University of Tübingen

[Hanqi Zhou]

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.

About the ML ⇌ Science Colaboratory
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 samples of our work, see

• our Resources page
• our GitHub organization

Your role
You will be contributing your experience toward developing models and software, coaching and giving advice, designing compelling explanations, and delivering them to postgraduate audiences. You will interact with scientists and ML researchers to set up joint projects, sometimes leading teams to carry them out. Our group is collegial and collaborative with access to exciting datasets and ML expertise in our research network that facilitate formulating projects aligned with our mission and your interests. Involvement in peer-reviewed publications is welcome but not required.

For details and to apply, see Researcher in scientific ML (m/f/d, E13 TV-L, 100%)