Open positions

Currently there are several positions to be filled.

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.

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

We are offering a position as

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

in Machine Learning for Neuroscience to be filled as soon as possible, with funding for three years with possible extension. The position is funded as part of the research program of the Cluster of Excellence “Machine Learning – New Perspectives for Science”. It will be based in the lab of Philipp Berens at the  Institute for Ophthalmic Research and will be jointly supervised by Philipp Hennig and Jakob Macke at the Department of Computer Science.

The successful candidate will perform research at the interface of machine learning and neuroscience, with the goal of leveraging recent advances in simulation-based inference and probabilistic numerics for improving simulations of mechanistic neuron models. Therefore, the successful candidate should have a MSc degree in computer science, computational neuroscience or related fields with prior experience in machine learning and clear interest in neuroscience as well as excellent programming skills in Python.

Tübingen is home to vibrant neuroscience and machine learning communities with ample opportunity for interaction with researchers at the Cluster of Excellence, the Tübingen AI Center and within the Tübingen Neurocampus. The Institute of Ophthalmic Research is part of the Center of Ophthalmology, covering various aspects of ophthalmic research from basic retinal neuroscience to the development of gene therapy and neuroprosthetics.

We offer employment with a salary and social benefits based on the collective agreement for public service employees in the academic and science sector, TV-L. As we strive for diverse groups, we particularly welcome applications from members of groups underrepresented in science. Also, the Eberhard-Karls-University of Tübingen promotes gender equality and therefore encourages female candidates to apply. Preferential status will be given to handicapped persons, if equally qualified.

Interested applicants should contact Prof. Dr. Philipp Berens (philipp.berensspam prevention@uni-tuebingen.de) providing a curriculum vitae and a 1 page motivation letter making a case why you are excited by the topic proposed, as well as names of at least two people that could provide letters of reference. Please compile your application in one single PDF-file and add [Neuro-ML Phd Position] to the subject line. Deadline for the application is July 31, 2021 (the position is advertised until 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 August 1, 2021. 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.