Open positions

Currently there are several positions to be filled.

Postdoctoral Research Fellow in AI & Data Science (f/m/d, E13 TV-L, 100%)

The AI & Data Science Fellowship Program, a cooperation between the University of Tübingen, distinguished as excellent by the Federal Government of Germany, and Boehringer Ingelheim, a leading pharmaceutical company, is inviting applications for a

Postdoctoral Research Fellow – AI & Data Science 
(f/m/d; E13 TV-L, 100%)

to work on cutting-edge and exciting AI & data science research topics that generate real added value for human and animal healthcare. The initial fixed-term contract will start as soon as possible and have a duration of 2 years with possible extension.

About the project

The position is available within the “Learning joint representations of EHR and omics data” project, a multidisciplinary interconnected effort to provide robust, data driven evidence to deal with multi-modal biomedical data. We aim to develop cutting-edge methods that will enable better disease subtyping, disease risk prediction, and patient stratification.

During the project, you will focus on large biobank data combining EHR and genetic information to learn the best joint representation that improves downstream analyses, including disease subtyping, disease risk prediction, and patient stratification.

  • Your collaborating scientist: Dr. Johann de Jong, Team Lead Statistical Modeling at Boehringer Ingelheim.
  • Your host: Pfeifer Lab led by Prof. Dr. Nico Pfeifer.

Your host research group has extensive knowledge at the interface between statistical machine learning, digital medicine, and computational biology. Prof. Nico Pfeifer is a PI at the excellence cluster Machine Learning: New Perspectives for Science, a fellow of the IMPRS for Intelligent Systems and receives further funding through the Tübingen AI Center.

Your profile

The ideal candidate will have:

  • a Ph.D. or equivalent in Machine Learning, Medical Informatics, Bioinformatics, Computer Science, Mathematics, or a related discipline. This position is also suited for researchers who have recently finished or are about to finish their Ph.D.
  • proven experience in data science and machine learning
  • a competitive track record of scientific publications in leading venues
  • strong programming/scripting skills (Python, C++, R, Java) and knowledge of ML frameworks (PyTorch, Keras, etc.)
  • a keen interest in interdisciplinary work
  • ability to communicate across disciplines
  • background / first experience in statistics, NLP methods, analysis of medical data (clinical data, molecular data, etc.), and/or high-throughput data (next-generation sequencing)
  • knowledge of databases (MySQL, NoSQL)

Our offer

What this position offers you:

  • exciting research in cooperation between a highly renowned university and a research-driven pharmaceutical company,
  • collegial work atmosphere,
  • remuneration in accordance with the TV-L (collective agreement for public employees of the German federal states) as well as all corresponding benefits,
  • international collaboration opportunities,
  • local & global networking opportunities incl. conferences, workshops, etc.
  • 30 days/year of paid vacation, 
  • flexible working hours, 
  • career mentoring,
  • opportunity to gain leadership experience by supervising your own student assistant,
  • extensive visa and onboarding assistance, 
  • discounted public transportation, etc.

We value diversity in science, and particularly look forward to receiving applications from women, non-binary people, and researchers from underrepresented groups across cultures, genders, ethnicities, and lifestyles. We actively promote the compatibility of science, work, studies, family life and care work. In case of equal qualification and experience, physically challenged applicants are given preference.

How to apply

Please send your application (including your motivation letter, curriculum vitae, certificates, representative publications, contact details of 2 academic references) with the subject “Application BI Postdoc” via e-mail to Prof. Dr. Nico Pfeifer: datascience@inf.uni-tuebingen.de.

Application deadline: April 1, 2024.

PhD position in Complexity Reduction, Explainability and Interpretability (KEI) (m/f/d; E13 TV-L, 73%)

We invite applications for a 3-year PhD or Postdoctoral position in Philosophy of AI at the University of Tübingen, Germany, to work on the project

Complexity Reduction, Explainability and Interpretability (KEI)

 (m/f/d; E13 TV-L, 73%, 36 mo., ideal start: 01.04.2024)

funded by the Heidelberg Academy of Sciences and Humanities (HAdW).

The position is limited to 3 years. The project was elected as part of the College’s 9th season entitled “Complexity Reduction: Principles, Methods and Challenges” and is thereby part of an interdisciplinary consortium with other funded projects.

The office of the successful candidate is intended to be at the AI building in Tübingen, within the Ethics and Philosophy Lab of the Cluster of Excellence Machine Learning for Science (ML Cluster), in the direct vicinity of several renowned institutions, including the Tübingen AI Center and Max-Planck-Institute for Intelligent Systems.

The project is also in collaboration with the SimTech Cluster of Excellence "Data-Integrated Simulation Science" at the University of Stuttgart through Dr. Klopotek, head of the group Many-Body Simulations and Machine Learning. It will thus also be associated with the Stuttgart Research Focus: Interchange Forum for Reflecting on Intelligent Systems.

Offer

  • The elected applicant will be supervised by and work with PD Dr. Eric Raidl
  • The contract will lie formally at the HAdW
  • The position shall be embedded in the Ethics and Philosophy Lab, and affiliated with the ML Cluster
  • The elected applicant will also work in close exchange with the physics part of the KEI-project, directed by Dr. Miriam Klopotek (University of Stuttgart, SimTech)
  • Travel budget
  • Costs for a laptop are covered

Responsibilities

  • Research and publication in Philosophy of AI in close collaboration with PD Dr. Eric Raidl
  • Support of PD Dr. Eric Raidl in research- and project- related tasks (communication, workshop organization, WIN-Kolleg meetings, etc)
  • Teaching is not mandatory but possible

Profile Requirements for the PhD Position

  • Excellent Master in philosophy, logic, or physics (or both)
  • Strong background in philosophy of science, philosophy of AI, epistemology, or physics
  • Knowledge of the topics of Explainable AI and Complexity (e.g., computational and algorithmic complexity, statistical physics)
  • Proficiency in English and German
  • Excellent organizational and communication skills
  • Experience in interdisciplinary discussion with scientists
  • Personal responsibility and ability to cooperate

Profile Requirements for the Postdoc Position

  • Excellent PhD in philosophy of AI, or philosophy of science, or philosophy of physics, or in logic
  • Two publications (published or accepted)
  • And the above-mentioned Profile Requirements for the PhD Position.

Please apply with the following documents –  cover letter, CV (maximally 2p each), copies of certificates, list of publications, and 1 written work for the PhD and 1 publication for the Postdoc Position  – as a single PDF; not exceeding 5 MB, to aitespam prevention@wsii.uni-tuebingen.de and in cc eric.raidlspam prevention@uni-tuebingen.de by February 15, 2024. In your cover letter, specify how your work and competences fit into the project. Incomplete applications will not be considered. Questions should be directed to eric.raidlspam prevention@uni-tuebingen.de. Interviews are planned for the end of February.

The HAdW and the university seek 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.

Full Professor (W3) of “Machine Learning in Science” (f/m/d)

The Computer Science Department of the Faculty of Science at Tübingen University invites applications for the position of

Full Professor (W3) of “Machine Learning in Science”

The position is due to commence as soon as possible.

The professorship is part of the Cluster of Excellence “Machine Learning: New Perspectives for Science“. A successful candidate is expected to carry out internationally competitive research in the field of machine learning in science. Possible research directions could be (but are not limited to) the following ones:
•    Theory of machine learning for science
•    Large language models for science
•    Causal inference in machine learning for science
•    Lifelong and continual learning
•    Experimental design with the help of machine learning

We expect the successful candidate to actively contribute to the cluster of excellence: by conducting collaborative research projects at the intersection between machine learning and science, and by contributing to some of the management tasks of the cluster. For further information on this cluster of excellence please visit www.ml-in-science.uni-tuebingen.de and www.machinelearningforscience.de.

In terms of teaching we request the new professor to participate in our international Master of Science program “Machine Learning”. Required qualifications include a PhD or equivalent degree as well as postdoctoral qualifications and teaching experience equivalent to the requirements of a full professorship.

The University of Tübingen is committed to equal opportunity, diversity and inclusion. Female scientists, in particular, are explicitly invited to apply, as are applicants from outside Germany. Applications from equally qualified candidates with disabilities will be given preference.

To submit your application, please upload your documents, including a research statement, on the university application portal: https://berufungen.uni-tuebingen.de. The closing date for applications is February 23, 2024. Enquiries may be directed to Tilman Gocht (tilman.gochtspam prevention@uni-tuebingen.de). General information on professorships, hiring processes, and the German academic system can be found here: https://uni-tuebingen.de/en/213700
 

HPC Cluster Administrator (f/m/d; E13 TV-L, 100%)

As part of the Cluster of Excellence “Machine Learning: New Perspectives for Science” at the University of Tübingen, we are looking for a

HPC Cluster Administrator (f/m/d; E13 TV-L, 100%)

on a permament position, starting as soon as possible.

About the role: high performance computing (HPC) is essential for competitiveness of science and industry. Modern AI-focused research is unthinkable without such infrastructure. As such, you will play a pivotal role in developing and administering infrastructure and tools for scalable AI-focused scientific computing. As part of a motivated team, you will support and maintain a heterogeneous cluster and work with the latest AI accelerators, networking and storage technologies. In addition, you will work and communicate with the users to help solve their issues when working on the cluster.

What You'll Do

  • Manage the day-to-day system administration of Linux-based cluster computing environments, including software and hardware installation/configuration.
  • Maintain cluster and node environment stability by reviewing monitoring tools.
  • Manage the day-to-day IT issues in support of our cutting-edge research community through the ticketing system.

What you will bring (position requirements):

  • Bachelor degree in information technology, applied computer science or computer engineering (or comparable degree).
  • Experience with HPC cluster manager & job scheduling software (e.g. Slurm, PBS, etc) 
  • Administration experience with Linux OS (e.g. SLES/RHEL/CentOS/Ubuntu etc.).
  • Good knowledge of the scripting language Bash and/or Python. 
  • Experience with Parallel file systems like GPFS/Lustre/Ceph/BeeGFS/Weka.
  • Independent, result driven work, demonstrates ownership and accountability.
  • English proficiency.

Relevant experience in some of the following technologies:

  • Some experience with automation tools for configuration management (e.g. Ansible, Puppet, Chef) and revision control systems (e.g. Git)
  • Experience with containers.
  • HPC system troubleshooting and support
  • Experience with network technologies

What We Offer

  • We offer remuneration in accordance with salary level E13 according to TV-L guidelines (collective wage agreement for the Public Service of the German Federal States) in addition to all customary benefits granted to employees working in Public Services. 
  • We are growing strongly and offer a vibrant working environment with more than 200 international researchers from all over the world.
  • Subsidization of the job ticket for public transport and attractive discounts on employee offer platforms.
  • Our regulations are family-friendly.

Contact:

In line with its internationalization agenda, the University of Tübingen welcomes applications from outside Germany. The University of Tübingen is committed to equal opportunity, diversity and inclusion and wishes to enhance the share of women and under-represented categories employed in research. Applications from equally qualified candidates with disabilities will be given preference. Women are expressly encouraged to apply. In principle, the position can be shared. Employment is based on the relevant provisions of university law. Please observe the applicable vaccination regulations. Presentation costs can unfortunately not be covered.

To apply, please send a cover letter and your CV in English and all relevant certificates in your application as a single PDF file to Dr. Kristina Kapanova at kristina.kapanovaspam prevention@uni-tuebingen.de  by 18.02.2024. For more information or questions about technical aspects of the position, please contact Dr. Kristina Kapanova at (kristina.kapanovaspam prevention@uni-tuebingen.de).

Student Research Assistant Position (40h per month), Machine Learning in Medical Image Analysis Group

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

Student Research Assistant Position (40h per month)

Project

Data scarcity is one of the major limiting factors preventing application of powerful machine learning algorithms to many medical applications beyond a handful of big public datasets. Cross-Domain Few-shot Learning (CD-FSL) offers the potential to exploit similarities between different medical image analysis datasets and leverage shared knowledge to learn previously unseen tasks more efficiently. However, CD-FSL is underexplored in medical image analysis. We recently released the MIMeta Dataset, the first medical image cross-domain few-shot learning benchmark, and started a challenge. With the L2L challenge we want to encourage the medical image analysis and machine learning communities to explore the potential of CD-FSL approaches in the promising application domain of medical image analysis, and to develop algorithms that are robust to the extremely high task and data diversity encountered in this domain. The L2L Challenge is an official MICCAI 2023 challenge. The International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) is the top conference in the domain of medical image analysis.

For more information visit our challenge website.

Your tasks

You will aid in building the evaluation system for the challenge and writing new features for the MIMeta and torchcross PyTorch libraries. Additionally you will help with technincal support for challenge participants.

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 few-shot learning and/or 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. Only currently enrolled students of the University of Tübingen can be considered for this position.

Several PhD positions in Machine Learning Based Data Anaysis of Scattering and Diffraction Data

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.