Open positions

The ML ⇌ Science Colaboratory invites applications for a

Researcher position (m/f/d, E13 TV-L, 100%)

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.

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.

Tübingen for research and life

Tübingen is a scenic university town on the Neckar river in South-Western Germany with an exceptionally high quality of life and a welcoming, diverse, and inclusive atmosphere. In Tübingen, you will find a young, international environment where most locals can speak English. Thanks to the University, four Max Planck institutes, the University Hospital, and Europe’s largest AI research consortium, Tübingen offers an intellectually stimulating atmosphere.

This network is a stone's throw away from your office at the brand-new Tübingen AI Research Center, where you can enjoy beautiful views, lots of social interactions with friendly colleagues, and excellent hardware at your desk and in our powerful ML Cloud.

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 emily.gabaldonspam prevention@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 2.04.2023 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.

The research group AutoML for Science of the Cluster of Excellence “Machine Learning" has an opening for a

PhD Position (75%, E 13 TV-L, m/f/d) | AutoML for Science

The contract will start as soon as possible and will have a duration of 3 years.

About the project. The application of ML relies on crucial design decisions that demand considerable expertise and resources. The research group focuses on methods to lift this barrier and make ML easy to use via hyperparameter optimization (HPO) methods and AutoML systems, aiming to study the following:

• How can we build the next generation of AutoML systems?
• How can we leverage recent advances in Deep Learning for AutoML systems?
• How can we construct meaningful benchmark problems?
• How can we develop practical HPO methods?
• What is needed to apply HPO/AutoML for scientific applications?

As a successful applicant, you will carry out research along these topics in the context of the Cluster of Excellence “Machine learning - New Perspectives for Science” as part of the newly established early career research group “AutoML for Science”. You will have ample opportunity to collaborate with other research groups, publish and present your scientific results at international venues.

Required qualifications

• An excellent MSc degree (or about to finish) in AI, ML, DL, computer science or a related discipline
• Solid knowledge of (and experience with) ML and DL methods
• Python knowledge with good working knowledge in applying/evaluating ML & DL methods

Knowledge in one or more of the following is beneficial: AutoML systems | Hyperparameter optimization | Bayesian Optimization | Large-scale evaluations | Deep Learning with tabular data

To apply please send the following documents (as PDF) to katharina.eggenspergerspam prevention@uni-tuebingen.de:

• Preferred starting date (including earliest and latest possible date) 
• CV & Transcript of records
• Research Statement (max one page) including why you want to start a PhD and why you want to do research in AutoML

Deadline for applications: April 16th, 2023

→ For further questions, please also do reach out to katharina.eggenspergerspam prevention@uni-tuebingen.de

About Tübingen. The University of Tübingen is one of few excellence universities in Germany. With its cluster of excellence “Machine Learning for Science”, embedded in the interdisciplinary research environment of the CyberValley, the Max-Planck-Institute, the ELLIS institute, and the Tübingen AI center, it provides a vibrant research environment, access to unique research facilities and great research opportunities.
The University seeks to raise the number of women in research and teaching and therefore emphatically calls on qualified women to apply. Disabled candidates will be given preference over other equally qualified applicants. The university is committed to equal opportunities and diversity. It therefore takes individual’s situation into account and asks for relevant information. The employment will be carried out by the central administration of the University of Tübingen.

The Machine Learning in Medical Image Analysis Group invites applications for a

Student Research Assistant Position (35h per month)

Project

A major limitation of deep learning for medical applications is the scarcity of labelled data. Meta-learning, which leverages principles learned from previous tasks for new tasks, has the potential to mitigate this data scarcity. However, most meta-learning methods assume idealised settings with homogeneous task definitions. The most widely used family of meta-learning methods, those based on Model-Agnostic Meta-Learning (MAML), require a constant network architecture and therefore a fixed number of classes per classification task.

We take a first step in the direction of making meta-learning algorithms, suitable for more realistic medical problems by investigating different strategies for training and testing with a variable
number of possible labels.

To this end, we are currently assembling a dataset and writing a PyTorch toolbox for testing meta-learning algorithms in a real-
istic medical setting. We aim to make our dataset and toolbox publicly available and to issue a challenge for the MICCAI 2023
conference. For more information visit our project website and watch the linked video.

Your tasks

You will aid in assembling the dataset for the challenge and writing the PyTorch medical meta-learning toolbox. Your main tasks will include collecting meta-information in a structured way for multiple source datasets and writing PyTorch dataset classes facilitating meta-learning on these datasets.

Your profile

• Good knowledge of Machine Learning and of Image Analysis/Computer Vision,
• Interest in working with medical imaging datasets,
• Interest in learning about and working on meta-learning,
• Proficiency in Python, PyTorch and Git.

What we offer

• HiWi salary according to the standard rates of the University of Tübingen,
• A desk space in the Tübingen AI Research Building,
• Insights into an exciting and trending research field,
• The possibility of contributing to a scientific publication.

How to apply

If interested, please contact Stefano Woerner (stefano.woernerspam prevention@uni-tuebingen.de) and attach your CV and transcript of records to apply.

The Schreiber Group at the University of Tübingen works on the physics of molecular and biological materials using X-ray and neutron scattering. A specialised sub-group is dedicated machine learning based data analysis of scattering and diffraction data. Currently we have several

PhD positions (m/f/d)

available. Candidates with experience or interest in neural networks and machine learning strategies to analyse scattering are especially encouraged to apply.

You should have good communication skills, attention to detail, and flexibility to work both independently as well as in a team. You should hold either a diploma/master degree in physics, physical chemistry, material science or have a background in computer science.

You will be part of challenging interdisciplinary projects that are integrated into major national and European research consortia such as the DAPHNE (DAta for PHoton and Neutron Experiments) NFDI consortium. We offer well-equipped laboratories, a highly collaborative international environment and affiliation with the Cluster of Excellence "Machine Learning: New Perspectives for Science" funded by the DFG and hosted at the University Tübingen. You will receive excellent training and for all our projects we offer the opportunity to perform research at international large-scale facilities (such as synchrotrons and neutron sources).  Details on our research as well as publications and background information can be found at http://www.soft-matter.uni-tuebingen.de/machine_learning_XRR.html and http://www.soft-matter.uni-tuebingen.de/machine_learning_GIWAXS.html

The University of Tübingen has ~ 28,000 students and more than 500 years of academic tradition. It has national excellence status as is ranked in the top 100 universities worldwide. You will benefit from a variety of training opportunities and language courses as well as the university’s graduate academy. See also https://uni-tuebingen.de/en/excellence-strategy.

Applications should include a cover letter describing research interests, achievements, motivation and capabilities; curriculum vitae; academic certificates; names and email addresses of two professional references (e.g., current or previous research advisors). The opening will remain valid until the position is filled.

The positions are available immediately. Salary will be determined according to the German collective wage agreement in public service. Please send your application within one PDF file to softmatterspam prevention@ifap.uni-tuebingen.de

The University aims to increase the proportion of women in research and teaching and therefore urges suitable qualified women scientists to apply. Qualified international researchers are expressly invited to apply. Severely disabled persons with equal aptitude will be given preferential consideration.