Open positions

Currently there are several positions to be filled.

PhD position (m/f/d; E13 TV-L, 65%) in Machine Learning for Education

In the Machine Learning in Education Network Project, an initiative of the Cluster of Excellence ML in Science, we are currently offering a

PhD position in machine learning for education (m/f/d; E13 TV-L, 65%)

in one of our four subprojects. Applications received until 15.10.2021 will receive full consideration. The position is limited to three years.

The project

Ever studied for an exam only to forget just days later? Psychophysical studies have shown how reviewing regularly leads to long-term recall, and on average the optimal review schedule spaces out repetitions exponentially in time, which is good news. However, although many apps make use of this fact, and students worldwide use them to schedule their learning of languages or even the medical curriculum, such spaced repetition apps usually lack flexibility to adapt to each individual learner, and they don't take advantage of the structure of the learning domain. For example, in programming you typically want to learn about variables before you learn about functions, classes or even macros, so it doesn't make sense to review the latter concepts before the former become familiar.

In this PhD you will address exactly these problems and develop machine learning methods that learn to schedule based on how the learner is doing so far and the structure of the learning domain, encoded as a knowledge graph. As part of this project, you may learn and use techniques like Bayesian structure induction, Gaussian processes, graph neural networks, and reinforcement learning in an environment with experts in those areas. By running controlled online tests with real human learners, we will be able to quantify how well the algorithm does relative to current approaches. And by writing a scheduler that any learning app can use, you will make your algorithm accessible to self-directed learners around the world.

You will have the opportunity to develop skills in probabilistic modelling, software engineering, and cognitive psychology working in a stimulating environment with peers and supervision from machine learning, cognitive psychology, computational linguistics / natural language processing and education sciences. We are excited about the intellectual challenges as much as about the practical impact in helping people learn better, and we hope you share our enthusiasm!

Your qualifications

You should have an excellent M.Sc. in a quantitative discipline, an affinity for software engineering, and a good understanding of probabilistic modelling for machine learning. The ideal candidate should be self-motivated, comfortable with both analytic and critical thinking, and passionate about science.

Please indicate in your application if you have prior experience with conducting experiments, computational modeling, and machine learning, including NLP. Programming (in e.g., Julia, Python or Javascript), software engineering (API design, databases, CI/CD), mathematics, communication (in English), and the ability to independently manage a project (of any type) should also be mentioned.

Important note: this project is very interdisciplinary! We only expect you to have experience in some of the subject domains, and bring in a lot of enthusiasm for the rest.

About us

The project is jointly led by Álvaro Tejero-Cantero (ML ⇌ Science Colaboratory) and Charley Wu (Human and Machine Cognition Lab), with co-supervision by Detmar Meurers (Theoretical Computational Linguistics Lab), Kou Murayama (Motivation Science Lab) and Ulf Brefeld (Information systems and Machine Learning).

This project is one of four subprojects in the Machine Learning in Education Network Project, an initiative of the Cluster of Excellence ML in Science to bring modern machine learning to education.

About Tübingen

Tübingen is a scenic university town on the Neckar river in South-Western Germany. The quality of life is exceptionally high, and the atmosphere is diverse and inclusive; most locals speak English. Tübingen offers excellent research opportunities due to the University, four Max Planck institutes, the University Hospital, and Europe’s largest AI research consortium. You can find out more about Tübingen and our work environment here.

Apply

We believe that diversity in age, abilities, sexuality, gender identity, ethnicity, perspectives and ideas makes not just for a richer life, but also for a better team outcome. And we know that people do their best work when they feel like they belong — included, valued, and equal. We strive for an environment where everyone brings their full selves to work knowing that they’ll be supported to succeed. If you share this vision, we would like to know you.

To apply, send in a single pdf file <lastname>.pdf with a cover letter detailing how your experience fits with the project and a clear description of your specific skills, your CV, the names and email addresses of 2-3 people who we can contact for reference, and unofficial copies of your University degrees. Please link to or enclose some code you have written (applications without code samples will not be considered). Send your application material to Elena (elena.sizana—uni-tuebingen.de). For questions about the job, write to Álvaro and Charley (alvaro.tejero / charley.wu—uni-tuebingen.de).

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.

E 13 TV-L

According to the general pay scale of German universities, the salary will be “E 13 TV-L at 65%”. There are 30 vacation days. Depending on your experience after the M. Sc., the University administration will place you in a certain level — check this salary calculator, with gross and net.

Postdoctoral position (m/f/d, E13 TV-L, 100%) Advanced analysis of scattering data: Machine learning and numerial tools

The “Physics of Molecular and Biological Matter” group, led by Prof. Frank Schreiber, at the Institute of Applied Physics, University of Tübingen, is currently looking for a

Postdoc position (m/f/d; E13 TV-L, 100%)
in Advanced analysis of scattering data: Machine learning and numerial tools

to be filled as soon as possible.

Using today’s computing power and software packages it has become possible to analyze large and multidimensional experimental scattering data. The process of converting these data into useful scientific information, however, can be challenging. Popular machine learning models, such as artificial neural networks, have recently shown significant advantages in terms of speed over other computational methods that are usually employed to extract the essential parameters of the investigated systems [1,2].

Within the field of soft matter physics, our group studies the fundamental structural properties, particularly the growth process, of organic thin films [3]. In this context, we collect X-ray scattering data using highly specialized synchrotron beamlines, e.g. at the ESRF in Grenoble or at Petra III in Hamburg. Modern area detector technology allows us to record enormous amounts of complex data, however, usually data analysis remains the bottleneck for the scientific output.

We offer a full-time position, starting from September 1, 2021. Candidates should have a PhD or an equivalent in Physics and have a background in computational methods and programming, with an interest in soft-matter physics.

Applications with the usual documents (including motivation letter, full CV, diploma(s)) should be sent in electronic form as a single PDF file to sekretariat.schreiberspam prevention@ifap.uni-tuebingen.de. The application deadline is August 1, 2021.

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. 

[1] A. Greco et al., Fast fitting ofreflectivity data of growing thin films using neural networks, J. Appl. Cryst. 52 (2019) 1342
[2] A. Greco et al., Neural network analysis of neutron and X-ray reflectivity data: Pathological cases, performance and perspectives.Mach.Learn.: Sci. Technol. (2021), in print

[3] C. Frank et al., Analysis of island shape evolution from diffuse x-ray scattering of organic thin films and implications for growth. Phys. Rev. B 90 (2014) 205401

Postdoctoral researcher position (m/f/d, E13 TV-L, 100%) Mathematical and Computational Population Genetics

The newly founded independent junior research group „Mathematical and Computational Population Genetics“, headed by Franz Baumdicker at the University of Tübingen has an opening for

Postdoctoral researchers (m/f/d, E13 TV-L, 100%)

The group develops and applies mathematical models and bioinformatic tools with a special focus on microbial evolution. We are interested in a variety of evolutionary scenarios including CRISPR Cas Evolution, Pan-Genome Evolution, Horizontal Gene Transfer, Fluctuationg Selection, and Cooperation in Bacteria.

The postdoc should be interested to operate at the intersection of Mathematical Population Genetics / Computational Biology / Bioinformatics using mathematical population genetics, phylogenetics, statistical analysis, machine learning algorithms, and large scale simulations to derive new mathematical results, develop open source software and apply them to (microbial) genome data.

For scientific questions please contact: franz.baumdicker@uni-tuebingen.de.

Candidate profiles we would love to see:

  •  PhD degree in (Bio-)Mathematics, Statistics, Bioinformatics, Computational Biology or a related field
  • Interest in interdisciplinary research
  • Strong mathematical preparation and interest and/or good computational skills (e.g. in Python, R)
  • Independent, responsible and committed work
  • Fluency in (scientific) English

What we offer:

  • Salary according to TV-L, E13 (100%)
  • The group is part of two Excellence Clusters (Controlling Microbes to Fight Infections & Machine Learning) in Tübingen, which offers an excellent research environment with plenty of potential collaboration partners.
  • Flexible starting date and the possibility to start the project remotely in the initial phase
  • Focus on research (no formal teaching duties)
  • Intense personal and scientific mentoring in an open and supportive environment
  • Integration into a young and agile research group
  • Responsibility to conduct your own research projects with a high amount of autonomy
  • The opportunity to visit and organize conferences, workshops and research visits to other universities and summer schools.
  • Possibilities to develop your own research ideas and mentor Ph.D. students

The University of Tübingen is an Equal Opportunity Employer with a strong institutional commitment to excellence through diversity. All qualified applicants will be considered for employment without regard to gender, race, color, national origin, sexual orientation, religion, disability, or age. Researchers from outside Germany are particularly encouraged to apply.

Applications and inquiries should be sent to franz.baumdickerspam prevention@uni-tuebingen.de.

Please send your application as a single PDF file and include a brief statement on your interests and experience, CV (including a possible list of publications and the contact info of two academic references), and university transcripts.

The review of applications will begin in July 2021 and continue until the positions are filled.

PhD position (m/f/d, E13 TV-L, 65%) Mathematical and Computational Population Genetics

The newly founded independent junior research group „Mathematical and Computational Population Genetics“ headed by Franz Baumdicker at the University of Tübingen has an opening for

PhD students )m/f/d, E13 TV-L, 65%)

The group develops and applies mathematical models and bioinformatic tools with a special focus on microbial evolution.

The PhD students will work at the intersection of Mathematical Population Genetics / Computational Biology / Bioinformatics using population genetics, phylogenetics, statistical analysis, machine learning algorithms, and large scale simulations to derive new theoretical results, develop open source software and apply them to (microbial) genome data.

We are interested in a variety of evolutionary scenarios including CRISPR-Cas Evolution and Dynamics, Pan-Genome Evolution, Horizontal Gene Transfer, Fluctuationg Selection, and Cooperation in Bacteria.

For scientific questions please contact: franz.baumdicker@uni-tuebingen.de.

Candidate profiles we would love to see:

  • Master's degree in (Bio-)Mathematics, Statistics, Bioinformatics, Computational Biology or a related field
  • Interest in interdisciplinary research
  • Strong mathematical preparation and interest and/or good computational skills (e.g. in Python, R)
  • Independent, responsible and committed work
  • Fluency in (scientific) English

What we offer:

  • Salary according to TV-L, E13 (65%)
  • Dissertation at the Faculty of Natural Sciences working with two advisors
  • The group is part of two Excellence Clusters (Controlling Microbes to Fight Infections & Machine Learning) in Tübingen, which offers an excellent research environment with plenty of potential collaboration partners.
  • Possibly integration into the DFG priority programme SPP2141, where appropriate
  • Focus on research (no formal teaching duties)
  • Intense personal and scientific mentoring in an open and supportive environment
  • Integration into a young and agile research group
  • The opportunity to visit conferences, workshops, other research groups and summer schools
  • Responsibility to conduct your own research projects with a high amount of autonomy
  • Possibilities to develop your own research ideas in a young and agile team

The University of Tübingen is an Equal Opportunity Employer with a strong institutional commitment to excellence through diversity. All qualified applicants will be considered for employment without regard to gender, race, color, national origin, sexual orientation, religion, disability, or age. Researchers from outside Germany are particularly encouraged to apply.

Applications and inquiries should be sent to franz.baumdickerspam prevention@uni-tuebingen.de.

Please send your application as a single PDF file and include- brief statement on the applicant's interests and experience, CV (including a possible list of publications and the contact info of two academic references), and university transcripts.

The review of applications will begin in July 2021 and continue until the positions are filled.

Postdoctoral Research Assistant (m/f/d, E13 TV-L, 100%) Clinical Bioinformatics

A position is open for a postdoctoral research assistant in the Clinical Bioinformatics group at the University Hospital/University of Tübingen. The available position focuses on development and application of machine learning approaches for the integration of clinical microbiome and single-cell sequencing/proteomic data modalities to achieve clinical decision support in the molecular tumor board.

We are looking for you as of now, or upon agreement, as a

Postdoctoral Research Assistant (E13 TV-L, 100%)

The position will involve research in the interdisciplinary consortium comprising researchers at Universities of Tübingen, Heidelberg, Ulm and Freiburg, Germany. Research in this consortium builds on the recent success of personalized treatment of cancer patients in interdisciplinary tumor boards.

This position is part of a proof-of-concept study aiming at the investigation of two subsets of primary liver cancer specimen using latest sequencing and tissue purification techniques to identify intratumoral microbiome/-immune/proteome/exosome signatures as surrogates for targeted therapy of primary liver cancer. The latter will be combined with matched peripheral blood mononuclear cells (PBMC). Aim of this study is to translate a reduced, specific signature of this combined analysis into the molecular tumor board for treatment stratification of liver cancer patients. This study is carried out by researchers from above institutions with a strong collaborative track-record and with expertise in running the respective MTBs, liver cancer tissue analyses, sample preparation, different sequencing technologies and data analyses including translational machine learning (e.g. Pfister et al., Nature 2021).

The candidate will apply and develop machine learning approaches to identify therapy response associated identify intratumoral microbiome/-immune/proteome/exosome signatures. Upon validation in independent patient cohorts, this information will then be used to stratify patients prior to therapy to maximize the therapeutic impact and to minimize adverse effects as well as to provide new personalized therapeutic targets for therapeutic intervention.

The ideal candidate brings along a degree that demonstrates an interdisciplinary background in both life and formal sciences. While a background cancer-/immune biology  and single cell proteomic experiments are a plus, a solid background in mathematics, statistics and programming is required to carry out the planned algorithm developments and data analysis. A fluent level of English is mandatory. We are looking for a highly motivated candidate with excellent communication skills that is capable of working in an interdisciplinary environment and can team up with scientists for experimental as well as computational analysis. The candidate should have a high degree of initiative. We offer work in a highly stimulating environment with state-of-the-art infrastructure, providing the successful applicant with unique opportunities to develop a strong interdisciplinary portfolio in both experimental and computational biology.

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

Applications with a motivation letter, full CV, diploma(s) and two contacts for further references should be sent online to manfred.claassenspam prevention@med.uni-tuebingen.de.

Postdoc position (m/f/d; E13 TV-L, 100%) in Ethics, Privacy and Fairness in Digital Education Environments

The Epistemology and Ethics of Machine Learning research group at the Cluster of Excellence “Machine Learning", University of Tübingen, invites applications for a

Postdoc (m/f/d; E13 TV-L, 100%) in Ethics, Privacy and Fairness in Digital Education Environments

to be filled in Summer/Fall of 2021. The position is limited to three years.

The application of ML methods in digital education raises significant ethical issues. Adaptive learning systems promise to be particularly useful for disadvantaged students without adequate family support and could thus contribute to the reduction of educational inequalities. Modern machine learning techniques promise a revolution in interactive and personalized education. However, students who stand to benefit the most are also the least able to advocate for themselves. Moreover, irresponsible implementation of algorithmic systems threatens to lower education quality and widen existing inequalities. Accordingly, the Innovation Fund “Machine Learning in Education” in the Cluster of Excellence “Machine Learning: New Perspectives for Science” in collaboration with the Hector Research Institute of Education Sciences and Psychology seeks to hire a Postdoc for fundamental research in the ethics and methodology of machine learning for education.

The  postdoc position (E13 TV-L, 100% - 36 Months) is to be filled (ideally) in Summer/Fall of 2021 and will be supervised by Konstantin Genin, Thomas Grote, Benjamin Nagengast and Bob Williamson. Close collaboration with the other members of the Innovation Fund “Machine Learning in Education” is expected. The position is funded for 3 years. Compensation is at minimum €4002/month brutto (€2379 netto) and increases according to experience. Funding for equipment, travel and other expenses is also available. Possible research areas include but are not limited to the following:

  1. Methodological Issues in the testing of ML algorithms. How do we learn whether algorithmic interventions are helpful or harmful? If an algorithmic intervention is helpful on average, how should its benefits be distributed among groups? Should randomized controlled trials be used to study the effects of algorithmic intervention? If so, how do we manage issues of privacy, equipoise and informed consent, especially when students may not be able to opt-out of such trials?
     
  2. Algorithmic Fairness. Algorithmic tutors make frequent and continual inferences about latent student features: mastery, motivation, attention, etc. These inferences inform what material is presented and how it is sequenced. Inequalities in algorithmic accuracy could allow discrimination to infiltrate the learning process. Mathematical trade-offs between competing algorithmic fairness notions only complicate matters. What are the relevant notions of fairness in algorithmic tutoring? How should tradeoffs between these notions be managed?
     
  3. Privacy, Respect and Autonomy. In educational ML, researchers will be able to collect unprecedentedly fine-grained information about students---up to the motion of their eyes. That could enable a revolution in personalized learning, but also poses significant threats to privacy and autonomy. Irresponsible or punitive use of these technologies threatens to be invasive, arbitrary and incompatible with respect for student autonomy. Is it possible to use these promising technologies without creating educational dystopias?

The position is, by its nature, extremely interdisciplinary. Therefore, we are open-minded about the background of potential applicants. Applicants holding a PhD in philosophy (esp. ethics),  statistics, machine learning, social science (e.g. psychology, psychometrics, economics, political science, sociology), education or allied fields are welcome to apply. The postdoc will be expected to collaborate with other groups in the “Machine Learning in Education” Innovation fund on issues of ethics and methodology.

Please upload the usual documents (cover letter; short (1 page) research proposal; academic CV including list of publications; writing sample and letters, if available) as a single PDF to this dropbox folder by the deadline of June 30, 2021. Please indicate in the cover letter which of your publications you would most like us to read and why you believe it is your best work. The group aims to decide on candidates by the end of the Summer.  Questions can be directed to konstantin.geninspam prevention@uni-tuebingen.de.

The University aims to increase the proportion of women in research and teaching and therefore urges suitably qualified women scientists to apply. The “Machine Learning in Education” group also welcomes applications from other groups underrepresented in philosophy and machine learning. Qualified international researchers are expressly invited to apply. Equally qualified applicants with disabilities will be given preference. The employment will be carried out by the central administration of the University of Tübingen.

Research Associate (m/w/d, E13 TV-L, 100%)

The University of Tübingen invites applications for the position of a

Research Associate (m/w/d, E13 TV-L, 100%)

starting at the next earliest possible date. The position is limited to 31.12.2025.

In the context of the Cluster of Excellence "Machine Learning: New Perspectives for Science", funded through the Excellence Strategy of the German Federal and State Governments, the Cloud Infrastructure at the University of Tübingen is being expanded.

The planned work is aimed at the expansion and operation of the ML Cloud (https://uni-tuebingen.de/en/199372) that has extensive CPU and GPU compute-capacities as well as a storage volume in the petabytes. The close collaborative partnership with the relevant scientific working groups is essential to be able to depict their workflows in the ML Cloud in a technically appropriate manner. Aside from research activities for the expansion and development of the ML Cloud, further responsibilities include supporting and further developing of the corresponding training program for users.

Candidates are expected to have knowledge about the basics of data management, to work scientifically independent, to be able to professionally interact with heterogenous user groups, to be flexible, to be able to work in a team and to have a good command of English. In addition, knowledge of cloud computing and virtualization as well as experience in operating complex IT-infrastructures and an interest in modern technologies are desirable. Didactic skills or experience in training scientists are also an advantage.

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. 

Applications with the usual documents should be sent in electronic form to the Central Office of the Cluster of Excellence "Machine Learning - New Perspectives for Science" (ml-in-sciencespam prevention@uni-tuebingen.de). Questions can be directed to Dr. Jens Krüger (jens.kruegerspam prevention@uni-tuebingen.de). The employment will be carried out by the central administration of the University of Tübingen.

Doctoral Research Assistant (m/f/d, E13 TV-L, 65%) Clinical Bioinformatics

A position is open for a postdoctoral research assistant in the Clinical Bioinformatics group at the University Hospital/University of Tübingen. The available position focuses on development and application of machine learning approaches for the simulation based reconstruction of differentiation processes of immune cells during viral infections.

We are looking for you as of now, or upon agreement, as a

Doctoral Research Assistant (m/f/d, E13 TV-L, 65%)

The position will involve research in the interdisciplinary consortium comprising researchers at Universities of Tübingen and ETH Zurich. Research in this consortium builds on its recent work on T cell exhaustion in chronic infection (Sandu et al. 2020, Cerletti et al. 2020, Gupta et al. 2020).

This position is part of an initiative investigating T cell exhaustion, an immune cell state associated with persistent viral infections as well as with impaired host immune defense in cancer affecting more than 2 billion people worldwide. The causal molecular mechanisms leading to exhaustion remain elusive due to the difficulty to account for the complex and dynamic interplay of regulators of T cell differentiation in vivo. We aim at identifying novel causal transcriptional mechanisms with an integrated multiplexed, in vivo, single-cell intervention screen and causal inference approach. We will perform a multiplexed single-cell CROP-seq intervention screen in conjunction with time series single cell RNA seq readout of antigen specific CD8+ T cells in the course of chronic infection. We propose deriving causal Markov models from the resulting data by comparative and integrative RNA velocity analysis building on our recent simulation based trajectory inference approach (Gupta et al. 2020). The method development and application to this end constitutes the core goal for the advertised position.

This approach will generate testable hypotheses on specific driver genes deciding on the fate of CD8+ T cells. In conjunction with our international partners we will validation the fate determining potential of these genes will be performed in vivo by selective targeting in LCMV-specific CD8+ T cells. Validated mechanisms and driver genes in our in vivo model system will motivate rational interventions to beneficially interfere with T cell exhaustion in the context of human chronic infections or cancer.T

The ideal candidate brings along a degree that demonstrates an interdisciplinary background in both life and formal sciences. While a background cancer-/immune biology  and single cell proteomic experiments are a plus, a solid background in mathematics, statistics and programming is required to carry out the planned algorithm developments and data analysis. A fluent level of English is mandatory. We are looking for a highly motivated candidate with excellent communication skills that is capable of working in an interdisciplinary environment and can team up with scientists for experimental as well as computational analysis. The candidate should have a high degree of initiative. We offer work in a highly stimulating environment with state-of-the-art infrastructure, providing the successful applicant with unique opportunities to develop a strong interdisciplinary portfolio in both experimental and computational biology.

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

Applications with a motivation letter, full CV, diploma(s) and two contacts for further references should be sent online to manfred.claassenspam prevention@med.uni-tuebingen.de.