Researcher in Scientific Machine Learning

Researcher in Scientific Machine Learning

University of Tübingen

Tübingen, Germany

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.

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.

Your profile

You possess a PhD degree in a quantitative discipline (mathematics, physics, computer science, etc), excellent programming skills, and hands-on experience training deep learning ML models.

All other qualifications below are just preferred; none of us walked in with all of them. If a few of these points apply to you, we want to talk to you!

• Ability to understand and explain recent machine learning research papers.
• Experience building and end-to-end training sophisticated deep learning models (e.g. graph neural networks, transformer-based NLP models, computer vision pipelines, …).
• Skill designing documented, composable APIs, vectorising/parallelising, using developer tooling (e.g. CI, git, docker...), etc.
• Fluency with the Python and/or Julia data science and machine learning stacks (e.g. scikit-learn, pandas, pytorch, jax, pyro, mlj, flux, turing...).
• Willingness to communicate complex ML methods to domain scientists and domain problems to ML researchers and drive to improve on current explanation formats by using interactive media.
• Aptitude for giving guidance to PhD and MSc students and working with senior collaborators.

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Germany      Academic      Computing/Programming      Data Science      Maths and Computing      University of Tübingen