Researcher in scientific ML

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.

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.

We are looking for a balanced team and will help each other grow where required.

What is important to us

We value empathy and seek individuals who genuinely care about each member of our team and our shared mission. We look for those who pay attention to details and appreciate excellence.

We understand that we all have different needs and responsibilities outside of work, so we are open-minded about flexible work schedules (within the limits permitted by law or set by the university) to accommodate different rhythms of life, including caring for the kids or the elderly.

We strive to constantly allocate time for learning and developing skills. To learn best not only from books but also from human interactions, we encourage kind, honest, and constructive feedback. We distribute work based on motivation and competence, not titles. And we stand behind our work as a team.

The University of Tübingen is committed to equal opportunities and diversity. It therefore takes the individual’s situation into account and asks for relevant information. We believe that diversity in ages, abilities, sexual orientations, gender identities, ethnicities, perspectives, and ideas makes not only for a richer life together, but also for a better team outcome. And we know that people do their best work when they feel like they belong — are included, valued, and treated equally. We try to build an environment where everyone brings their full selves to work knowing that they’ll be supported to succeed. We hope you’ll join us.

How to apply

For questions about the job or to apply, write to Emily at emily.gabaldon@uni-tuebingen.de. Applications should include, in a single pdf file

• a letter explaining why you'd like to be part of the team and how you would like to contribute to our goals,
• your curriculum vitæ and contact details of two or three people who have worked with you,
• links to code you have written, talks you have given, and papers you have published, if applicable, and
• a transcript of records of your last two degrees.

Applications received until 02.04.2023 will receive full consideration.

The university seeks to raise the number of women in research and teaching and therefore urges qualified women academics to apply for these positions. Equally qualified applicants with disabilities will be given preference. The employment will be carried out by the central administration of the University of Tübingen.

Contract

According to the general pay scale of German universities, the salary will be in the “E 13 TV-L” grouping, with the specific level depending on experience. The position is initially financed until 31.12.2025.

Tübingen for Research and Life

Tübingen is a picturesque, green, lively (and lovely) town on the Neckar valley.

About one in three of the 90.000 people living here is a student. The atmosphere is inclusive, welcoming and international, and you don't need to speak German to live here.

The Machine Learning Cluster is situated in the northern part of the town, easily accessible by bus, bike or even a nice pedestrian walk through gardens and woods. The Max Planck Institutes are just a few steps away.

Our building is brand new...

... we have the best coffee around, amusement options, showers (if you want to go for a run during lunch break)...

... meeting rooms with powerful hardware, and recharging views.