# Join us at the Machine Learning ⇌ Science Colaboratory (Tübingen, Germany)

**URL:** <https://discourse.julialang.org/t/join-us-at-the-machine-learning-science-colaboratory-tubingen-germany/95440>\
**Category:** Jobs\
**Created:** [March 2, 2023, 12:44pm UTC](https://discourse.julialang.org/t/join-us-at-the-machine-learning-science-colaboratory-tubingen-germany/95440 "2023-03-02T12:44:11Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![sethaxen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sethaxen/32/35604_2.png) [@sethaxen](https://discourse.julialang.org/u/sethaxen)\
**Post date:** [March 2, 2023, 12:44pm UTC](https://discourse.julialang.org/t/join-us-at-the-machine-learning-science-colaboratory-tubingen-germany/95440/1 "2023-03-02T12:44:11Z")

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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](https://mlcolab.org/resources) page and our [GitHub organization](https://github.com/mlcolab/). We are active contributors to the Julia community and would love to receive applications from fellow contributors.

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/](https://uni-tuebingen.de/universitaet/stellenangebote/newsfullview-stellenangebote/article/researcher-in-scientific-ml-m-f-d-e13-tv-l-100/)

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**Author:** ![sethaxen](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/sethaxen/32/35604_2.png) [@sethaxen](https://discourse.julialang.org/u/sethaxen)\
**Post date:** [March 29, 2023, 7:51am UTC](https://discourse.julialang.org/t/join-us-at-the-machine-learning-science-colaboratory-tubingen-germany/95440/2 "2023-03-29T07:51:34Z")

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Just 4 days left to apply to join our team! If you have any questions, feel free to contact us!