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PostdoktorandIn (m/f/d; E13 TV-L) oder DoktorandIn (m/f/d; E13 TV-L, 65%)

The “Machine Learning in Medical Image Analysis” lab of the Cluster of Excellence “Machine Learning: New Perspectives for Science” is currently looking for a

Postdoctoral Researcher (E13 TV-L, m/f/d) or PhD Student (E13 TV-L, 65%, m/f/d)

starting as soon as possible. 

How can we bring machine learning technology to clinical medical imaging practice? This is the main question the Machine Learning in Medical Image Analysis (MLMIA) group led by Dr. Christian Baumgartner tries to answer. Far from being solved, this question requires novel and creative approaches to numerous hard machine learning challenges. The main research focus of the MLMIA group is the development of methods that enable collaboration between humans and AI systems, in particular techniques for uncertainty quantification, interpretabiliy of predictions, and human-in-the-loop systems. A further focus is the application of generative modeling approaches to large medical imaging cohorts in order to better understand physiological and pathological processes and their connection to extraneous factors.

The successful PhD candidate will be working on interpretatable machine learning (but there is a lot of flexibility to accommodate the candidates personal interests). The successful postdoc candidate will be given the opportunity to shape their own research agenda within the confines of the group's motivation.

What we are looking for
You have a strong academic background and hold a PhD, M.Sc., or equivalent degree in a quantitative discipline such as computer science, physics, mathematics, statistics, electrical engineering, or biomedical engineering. You are self-driven, curious, and enjoy analytical thinking. You have a strong motivation to do machine learning research as well as a keen interest to solve real-world clinical problems. Ideally, you have prior experience with machine learning, and strong programming skills in Python. Prior experience working with medical imaging data is a plus, but not required.

What we offer
The MLMIA group is located in the machine learning building of the University of Tübingen, together with the Cluster of Excellence “Machine Learning: New Perspectives for Science”, which the group is part of, and Tübingen AI Center. As such, the successful candidate will be embedded in an extraordinarily vibrant machine learning community in which regular exchanges of ideas and collaborations are common. The MLMIA group also greatly values the direct exchange with clinical partners from the University Hospital Tübingen with which we have several ongoing collaborations.

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, inclusive, and 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 here: https://www.tuebingen.de/en/

How to apply
Please send a cover letter, your CV, the names and email addresses of 2-3 referees, and copies of your University transcripts to Christian Baumgartner (christian.baumgartnerspam prevention@uni-tuebingen.de). If you have any questions about the position, please do not hesitate to contact Christian directly. 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. Please submit your application by May 2nd, 2021.

Doktorandenstelle (m/f/d; E13 TV-L, 65%) in Robust Machine Learning for Medical Image Analysis

The Machine Learning in Medical Image Analysis group (led by Christian Baumgartner) and the Machine Learning group (led by Matthias Hein) at the University of Tübingen, Cluster of Excellence ‘Machine Learning: New Perspectives for Science’ invite applications for an open

Doctoral-Position (m/f/d, E13 TV-L; 65%) in Robust Machine Learning for Medical Image Analysis

to be filled as soon as possible.

Project description
The aim of this project is to develop rigorous, mathematically founded techniques to assess the robustness of automated medical image analysis systems and to investigate methods for providing provable guarantees of an algorithms performance under variations in the image acquisition process.

Medical imaging data frequently are subject to systematic changes in appearance originating from different acquisition parameters or different imaging hardware. Unfortunately, modern deep learning systems have been shown to be extremely sensitive to such variations, to the point where an algorithm trained with data from one hospital, may not work on data acquired at a different hospital. Formally assessing the robustness of machine learning methods, building more robust techniques, and providing guarantees for the performance of such techniques is of utmost importance for these methods to be eventually deployed in clinical practice. Thus, the successful candidate will contribute directly to one of the big unsolved problems hindering wide-spread adoption of AI technology for medical image analysis.

Who we are looking for
You are curious, enjoy analytical thinking and have a passion for science. You have a strong academic background, are motivated to do machine learning research, and have keen interest to solve real-world clinical problems. You hold a M.Sc. degree (or similar) in machine learning, mathematics, statistics, physics, computer science, or similar fields. Ideally, you have prior experience with deep neural networks, and strong programming skills in Python.

What we offer
This is a project jointly supervised by Prof. Matthias Hein and Dr. Christian Baumgartner and thus is truly at the intersection between state-of-the-art machine learning and medical image analysis. The successful candidate will work at the Cluster of Excellence "Machine Learning - New Perspectives for Science" and will benefit from this vibrant research environment as well as from the activities and events organized by the cluster and associated institutions.

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, inclusive, and 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 here: https://www.tuebingen.de/en/

How to apply
Please send a cover letter, your CV, the names and email addresses of 2-3 referees, and copies of your University transcripts to Christian Baumgartner (christian.baumgartnerspam prevention@uni-tuebingen.de). If you have any questions about the position, please do not hesitate to contact Christian directly. The university seeks to raise the number of women in research and teaching and therefore urges qualified women scientists 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. Please submit your application by May 2nd, 2021.

Doktorandenstelle (m/f/d; E13 TV-L, 80%) in Compositional Data Synthesis

The Bosch Industry-on-Campus Lab, a research collaboration between the University of Tübingen and Bosch Center for AI (BCAI), invites applications for an open

PhD position (m/f/d; E13 TV-L, 80%)

in Compositional Data Synthesis. The aim of this project is to learn how to synthesize new, previously unseen visual scenes through object compositionality. A bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked. In this project, you will investigate object compositionality as an inductive bias for deep generative models, such as generative adversarial networks (GANs). Specifically, you will focus on how to generate novel unseen compositions of objects present in the training set.

You will be jointly supervised by Prof. Dr. Zeynep Akata from the University of Tübingen side (Cluster of Excellence 'Machine Learning') and Dr. Anna Khoreva from the Bosch side.

The position is available immediately (but start date is negotiable), the contract is initially for three years, and remunerated according to the German salary scale 13 TVL.

What are you going to do?

As part of the Bosch Industry-on-Campus Lab at the University of Tübingen, you are going to carry out AI research and develop novel deep generative models, learning to synthesize new data samples through the notion of compositionality. There will also be regular visits and interactions with researchers at Bosch Center for AI, who have an office on campus. At the University of Tübingen you will be supervised by Prof. Dr. Zeynep Akata and Dr. Anna Khoreva.

Your tasks will be to:
  • Develop new computer vision and/or deep machine learning methods on compositional data synthesis;
  • Collaborate with other researchers within the lab and BCAI Research;
  • Complete and defend a PhD thesis within the official appointment duration of three years;
  • Regularly present internally on your progress and help Bosch write patent applications to protect inventions from the lab when requested.
  • Regularly present intermediate research results at international conferences and workshops, and publish them in proceedings and journals;
  • Potentially assist in relevant teaching activities.
What do we require?
  • Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or related field;
  • Strong background in computer vision and/or machine learning;
  • Excellent programming skills, preferably in Python;
  • Prior experience of working with deep learning libraries, such as PyTorch or TensorFlow;
  • Solid mathematics foundations, especially in probability theory, statistics, calculus and linear algebra;
  • High motivation and creativity;
  • Strong communication, presentation and writing skills and excellent command of English.

Prior publications in relevant vision and machine learning venues as well as experience working with deep generative models (e.g. VAEs, GANs, Flows) will be advantageous for your application.

Application

The University of Tübingen is an equal-opportunity employer. We prioritize diversity and are committed to creating an inclusive environment for everyone. We seek to increase diversity and the number of women in areas where they are under-represented and therefore explicitly encourage women to apply. We are also committed to recruiting more people living with disabilities and strongly encourage them to apply. The employment will be carried out by the central administration of the University of Tübingen.

Do you recognize yourself in the job profile? Then we look forward to receiving your application by May 31st, 2021. Please note the position will be filled as soon as an appropriate candidate is found. 

Your application should consist of a single PDF file <lastname_firstname>.pdf containing:

  • A two-page motivational letter, which: 1) explains why you would like to join us and 2) describes the research topics that excite you and that you would like to pursue in your PhD;
  • Your CV, with details of publications and conference participations (if applicable);
  • A copy of your Master’s degree certificate, if you already have one;
  • Unofficial transcripts of all of your university studies (BSc and MSc), as well as a translation into English and explanation of grading system (if needed);
  • Letters of recommendation and/or contact details of 2-3 referees;
  • Link to github or enclosed code sample you have written;
  • Optionally, additional documents such as a thesis, published papers, or project portfolios.

You may apply by sending your documents to eml-sekretariatspam prevention@inf.uni-tuebingen.de.

Doktorandenstelle (m/f/d; E13 TV-L, 65%) Maschinelles Lernen & Erneuerbare Energien

The Department of Computer Science at the Eberhard Karls University Tübingen invites appli­cations for a position in the Cluster of Excellence ‘Machine Learning: New Perspectives for Science’:

1 Doctoral-Position (m/f/d, E13 TV-L; 65%)

The position is available immediately (but start date is negotiable), the contract is initially for three years, and remunerated according to the German salary scale 13 TVL.

Providing heating and hot water for buildings contributes considerably to the carbon dioxide emissions. Solar thermal systems (STS) could substantially reduce these emissions. The project will use machine learning on existing data-logs of STS and will combine those data with weather data to optimize efficiency and maintenance of the systems. The PhD position is embedded in a joint initiative of four projects that all focus on spatiotemporal environmental data (project: ‘Modeling and Understanding Spatiotemporal Environmental Interactions’) within the Cluster of Excellence ‘Machine Learning: New Perspectives for Science’. The project partners for our subproject are:

Applicants must hold a MSc-degree (or equivalent) in machine learning, mathematics, statistics, physics, computer science, or similar fields. We expect knowledge and interests in environmental topics and programming skills (e.g., Python / R / C/C++).

Tübingen is a beautiful, small University-town with one of the famous old Universities of Europe (http://www.uni-tuebingen.de/en/university.html). It has a strong and lively research community committed to Machine Learning. Tübingen is a cultural center and right at the beautiful Swabian Alb which allows for plenty of outdoor activities. It is also very close to the large cultural center of Stuttgart.

Disabled candidates will be given preference over other equally qualified applicants. The University seeks to raise the number of women in research and teaching and therefore urges qualified women to apply.

For your application, please send the following documents:

  • Curriculum vitae
  • Copies of all university and  school certificates (starting with matriculation-relevant school certificate)
  • Copy of your passport (for non-German applicants)
  • Short sketch of your research interests (1 page max)
  • Letters of recommendation and/or contact details of 2-3 referees.

Please address your application by Email (as one single PDF-file) to: ec-sekretariat(at)inf.uni-tuebingen.de. Deadline for application is April 20th, 2021 (the position will be open until filled).

Formal employment procedures will be carried out by the University’s Central Administration. If you have any questions please contact us by email ec-sekretariat (at) inf.uni-tuebingen.de

Doktorandenstelle (m/f/d; E13 TV-L, 65%) Human and Machine Cognition

The “Human and Machine Cognition” lab of the Cluster of Excellence “Machine Learning: New Perspectives for Science” and the “Tübingen AI Center” invites applications for an open

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

to be filled as soon as possible. The position is limited to three years.

About the group:

The HMC lab is led by Dr. Charley M. Wu and operates at the intersection of Human Cognitive Science and Machine Learning research. The focus of this position will be to study the strategies that humans use to learn from and interact with other people in social settings. Potential topics include the integration of social and individual information, computationally tractable implementations of Theory of Mind inference, and cumulative cultural evolution in online communities, in addition to the interests of the candidate.

Members of the lab are working on a diverse set of topics including, structure learning in planning and search, developmental changes in learning and exploration, inductive biases in compositional learning, and many more. Our research methods include online experiments (commonly in the form of interactive games), lab-based virtual reality experiments, computational modeling of behavior, evolutionary simulations, developmental studies (comparing children and adults), fMRI/EEG, and analyzing large scale real-world datasets. We have a rich collaboration network of researchers from Harvard, Princeton, UCL, and several Max Planck Institutes around Germany. To find out more, visit the lab website at www.hmc-lab.com.

About the position:

The candidate should hold a MSc degree in cognitive science, computer science, psychology, computational neuroscience, statistics, or any relevant discipline. The ideal candidate should be self-motivated, comfortable with both analytic and critical thinking, and have a passion for science. Please indicate in your application if you have prior experience with conducting experiments, computational modeling, machine learning, and/or neuroimaging (EEG/fMRI). Skills in computer programing languages (e.g., R, Python, Matlab, Javascript, Java, etc.), mathematics, writing (in English), and the ability to independently manage a project (of any type) should also be mentioned.

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, inclusive, and 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 here: https://www.tuebingen.de/en/

How to apply:

Please send a cover letter, a description of your research interests (max 1 page), your CV, the names and email addresses of 2-3 referees, and unofficial copies of your University degrees to Charley Wu (charley.wu@uni-tuebingen[dot]de). If you have any questions about the position, please do not hesitate to contact Charley directly. 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. Please submit your application by May 15th, 2021.

Doktorandenstelle "Maschinelles Lernen in den Klimawissenschaften" (m/f/d; E13 TV-L, 65%)

The Machine Learning in Climate Science research group at the Cluster of Excellence “Machine Learning", University of Tübingen, invites applications for a

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

to develop seasonal forecasts of extreme rainfall events over Germany and western Europe using spatiotemporal artificial neural networks (STANNs). The position is funded for three years starting no later than 1 September 2021 and, along with three other PhD projects, is part of the mini-graduate school Modeling and Understanding SpatioTemporal Environmental INteractions (MUSTEIN).

MUSTEIN aims at developing machine learning (ML) techniques that reliably learn explainable models of critical aspects of four highly interacting spheres, focusing on (i) seasonal weather dynamics (P1), (ii) river water discharge (P2), (iii) soil erosion (P3), and (iv) solar thermal systems (P4). The targeted ML-based systems will potentially allow us to (i) predict environmental system dynamics more accurately and for longer periods into the future, (ii) anticipate future climatic developments and prepare accordingly, (iii) partially control the developments, and (iv) explain the hidden causes and influences in an accessible, causal manner.

Qualifications

You should hold a MSc degree (or should have one by June 2021) in computer science, physics, mathematics, statistics, geoscience, earth science, meteorology, or any other relevant discipline. A background in probability and statistics, along with a basic knowledge of artificial neural networks and deep learning is preferable. You should be capable of independent, creative, and critical thinking, and you should be willing to tackle challenging problems which do not offer easy resolutions at first sight. Ideally, should you have experience in scientific programming, along with some prior experience of working with deep learning libraries such as PyTorch or TensorFlow. If you have prior research experience, such as from a Master’s thesis project or in the form of a conference paper or a manuscript, it will add significantly to the strength of your application.

Role

You will work towards a PhD degree, to be completed within three years from the start of funding. You will conduct supervised scientific research within Project P1: Seasonal Weather Forecast of MUSTEIN. This will involve developing new methods and techniques to analyse extreme rainfall over western Europe using STANNs and to develop a probabilistic seasonal forecast scheme that can be used to anticipate the frequency of extreme rainfall events up to six months in advance. The project will necessitate the analysis of large-scale spatiotemporal climate data sets and the application of state-of-the-art deep learning methods. You will be required to regularly communicate your scientific progress in the form of conference presentations and academic journal articles. Your project will be supervised by Dr. Bedartha Goswami (Machine Learning in Climate Science), in collaboration with Prof. Dr. Martin V. Butz (Neuro-Cognitive Modeling Group), Prof. Dr. Hendrik Lensch (Computer Graphics), and Dr. Nicole Ludwig (Machine Learning in Sustainable Energy Systems). You will take active part in joint research activities with these research groups and also others that are involved in MUSTEIN.

Tübingen

Tübingen is a picturesque town home to one of Germany’s oldest universities dating back to 1477. It also houses four Max Planck Institutes, four Helmholtz Research Centres, three institutes of the Leibniz Association, the University Hospital, and a significant portion of the Cyber Valley initiative, Europe’s largest AI research consortium. The town has a dynamic and intellectually stimulating atmosphere that will allow you to grow as a researcher and as a person. The people here are welcoming, diverse, and inclusive, and most locals speak English. Theater, concerts, exhibitions, and festivals are a regular feature in the town’s calendar and there are numerous possibilities of going out on nature excursions in the surrounding Swabian Jura.

Application

Your application should consist of a single PDF file <lastname_firstname>.pdf containing:

  • a cover letter, explaining why you would like to join us,
  • a statement of research interests (max 2 pages), briefly outlining the research topics that excite you and that you would like to pursue in your PhD,
  • your CV, with details of publications and conference participations (if applicable),
  • a copy of your Master’s degree certificate, if you already have one,
  • a copy of your academic transcripts from your Master’s degree program, and
  • two letters of reference.

Barring the degree certificate, transcripts, and letters of reference, the document should be typeset using left-aligned Times New Roman, Arial, or Calibri typefaces, 12pt font size, single line spacing, and 1 inch margins.

Please send in the application file to Dr. Bedartha Goswami at bedartha.goswamispam prevention@uni-tuebingen.de by 15 May 2021. Shortlisted candidates will be informed by the end of May and interviews will be held virtually in the first week of June. We expect you to start in August preferably, but no later than 1 September 2021.

Equal opportunities

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.

Doktorandenstelle "Maschinelles Lernen im Bildungsbereich" (m/f/d; E13 TV-L, 65%)

PhD position in machine learning for education

Applications received until 30.04.2021 will receive full consideration. The position is limited to three years.

About the project

The  aim of this project is to develop machine learning methods that empower human learners. Specifically, your work will involve conceptualizing, implementing, and testing an adaptive learning system, which leverages the latent relational structure of knowledge to provide self-directed learners with an effective curriculum. You will have the opportunity to develop skills in probabilistic modelling, software engineering and cognitive psychology.

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 have a passion for science. Please indicate in your application if you have prior experience with conducting experiments, computational modeling, and machine learning. Programming (e.g., Python, Julia, 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.

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). The project is part of a larger initiative at the Cluster of Excellence "Machine Learning in Science" to investigate how machine learning can help modern education. There will be many opportunities to interact with world-class ML experts and other students working on learning analytics, personalization, and machine learning ethics.

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, inclusive, and 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. We hope you’ll join us.

To apply, send in a single pdf file <lastname>.pdf  with a cover letter detailing how your expertise and experiences fit with the project (with clear descriptions 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. For support with the application contact Elena (elena.sizanaspam prevention@uni-tuebingen.de). For questions about the job, write to Álvaro and Charley (alvaro.tejerospam prevention@uni-tuebingen.de / charley.wuspam prevention@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% occupation rate. There are 30 vacation days. Depending on your experience after the MSc, the University administration will place you in a certain level → Salary calculator, with gross and net.

PostdoktorandIn als LeiterIn einer Forschungsgruppe (m/f/d, E14 TV-L)

The chair for 'Foundations of Machine Learning Systems' (Robert C. Williamson) of the Department of Computer Sciences of the University of Tübingen invites applications for the position of a

Postdoc as Research Group Leader (m/f/d)

to be filled as soon as possible. The position is paid according to E14 TV-L (100%) and is limited to three years.

I am interested in machine learning from a broad systems perspective, and am looking for a postdoctoral scientist who is both interested in the mathematical foundations of machine learning systems, as well as how these mathematical foundations relate to the socio-technical systems that machine learning technology is becoming increasingly embedded into. The work is expected to require a blend of sophisticated mathematics as well as new conceptual ideas. Specific topics to be explored by the successful candidate include:

  • Information: Information theoretic limits of machine learning, from a geometric perspective, extending for example my work on information processing theorems, and the geometry of losses (see this earlier work too).  The overarching goal of this work is to build a theory of machine learning problems (an analogy is that of the development of functional analysis in the 20th century relative to the 19th century notion of a function as a formula)
  • Data: New theories of data - existing machine learning is largely built upon probability theory. But there are many reasons why this is not adequate. Richer theories require more sophisticated mathematics to handle situations (for example) where relative frequencies are not stable.
  • Society: The relationship between the mathematical formalisms used to describe data, and hence what machine learning algorithms do, and societal constraints such as fairness (extending for example my work on Fairness Risk Measures). Consequently, determination of theoretical limits to fairness (building for example on my work on the cost of fairness) becomes important.
  • Context: The conceptual / philosophical basis for machine learning systems; in particular, how can one represent the context in which data is gathered, and in which decisions or outputs are deployed? How can one reason about this, and how can one relate this to the mathematical formalisms implicit in the earlier bullet points?  Further develop the ideas sketched in my HDSR commentary.

Candidates should hold a PhD in a suitable discipline, including computer science, mathematics, engineering, any quantitative science, or philosophy (if suitably quantitative).

Applications with the usual documents (motivation letter, CV, transcripts of records of all your degrees, your favorite publication) should be sent in electronic form (as a single PDF, at most 5 MB) to charlotte.wennerspam prevention@uni-tuebingen.de by 30 April 2021. Applications should include the names of three referees who can comment on the candidate’s scientific work. Questions can be directed to bobwilliamsonozspam prevention@icloud.com

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.

PostdoktorandIn (m/f/d, E13 TvÖD) in ‘Machine Learning for Medical Image and Data Analysis’

The Empirical Inference Department of the Max-Planck Institute for Intelligent Systems (Prof. Dr. Bernhard Schölkopf, Prof. Dr. Sergios Gatidis), associated with the Cluster of Excellence 'Machine Learning – New Perspectives for Science' is looking for a

Postdoctoral Researcher (E13 TvÖD, m/f/d) in ‘Machine Learning for Medical Image and Data Analysis’

starting as soon as possible. The initial fixed-term contract will be for 3 years.

Automated analysis of medical image data has undergone impressive developments over the past decade mainly due to the application of deep learning methods. Despite these advances, machine learning methods are still rarely used in clinical practice due to underperformance in real-life settings. Our goal is to investigate how state-of-the-art machine learning methodology can be applied to medical images to overcome these challenges by better understanding their structure and content.

The position is part of the Mini-Graduate School “Uncovering the Inner Structure of Medical Images through Generative Modeling” within the Cluster of Excellence 'Machine Learning – New Perspectives for Science'.

Our interdisciplinary group works on the development and application of machine learning methodology for medical data analysis focusing on representation learning and causality. We have strong connections to collaboration partners at the University of Tübingen and the associated University Hospital. We aim to work in an interdisciplinary, collaborative and supportive work environment which emphasizes diversity and inclusion.

Tübingen has an internationally renowned research community in artificial intelligence, machine learning and computational neuroscience, including the Excellence Cluster Machine Learning, the Cyber Valley Initiative, the Tübingen AI Center, and the new MSc Program Machine Learning. We are situated in the Max Planck Institute for Intelligent Systems, in close proximity to the Max Planck Institute for Biological Cybernetics and the AI Research Building.

The position is open to candidates who have a PhD in in a quantitative discipline (e.g. computer science, maths, statistics, physics, electrical engineering, computational neuroscience), a genuine interest in interdisciplinary work at the interface of machine learning and medicine, and strong programming skills (ideally Python/PyTorch). Prior experience in deep learning, and/or in analysing medical data is advantageous.
The Max-Planck Society seeks to raise the number of women in research and therefore explicitly encourages them to apply for this position. Equally qualified applicants with disabilities will be given preference. We strive for gender equity and welcome applications from scientists from all backgrounds.

Please submit your application materials to Lidia Pavel (lpavelspam prevention@tuebingen.mpg.de), with subject 'Application: Postdoc Medical Imaging'. Please include a CV, a brief statement of research interests, contact details of two referees and a work sample - anything that is genuinely your own work, e.g. a thesis, computer code, a research manuscript, an essay, or a publication. We expect relevant prior publications. Application deadline: April 30th 2021

DoktorandIn oder PostdoktorandIn (m/f/d; E13 TV-L) in 'Deep learning for studying population codes in the human brain'


The Chair for 'Machine Learning in Science' (Prof. Dr. Jakob Macke) in the Cluster of Excellence 'Machine Learning – New Perspectives for Science' and in the Department of Computer Science at Eberhard Karls University Tübingen is currently looking for a

 

PhD Student or Postdoctoral Researcher (E13 TV-L, m/f/d) in 'Deep learning for studying population codes in the human brain'

starting as soon as possible. The initial fixed-term contract will be for 3 years with possible extension.

How do neural circuits in the human brain recognize objects, persons and actions from complex visual stimuli? To address these questions, we will develop deep convolutional neural networks for modelling how neurons in high-level human brain areas respond to complex visual information. We will make use of a unique dataset of neurophysiological recordings of single-unit activity and field potentials recorded from the medial temporal lobe of epilepsy patients. Our tools will open up avenues for a range of new investigations in cognitive and clinical neuroscience, and may inspire new artificial vision systems.  

The position is part of the BMBF-funded project DeepHumanVision in collaboration with the 'Dynamic Vision and Learning' Group at TU Munich (Prof. Dr. Laura Leal-Taixé) and the Cognitive and Clinical Neurophysiology Group at University Hospital Bonn (Prof. Dr. Dr. Mormann).  

Our group develop computational methods that help scientists interpret empirical data, with a focus on basic and clinical neuroscience research. We want to understand how neuronal networks in the brain process sensory information and control intelligent behaviour, and use this knowledge to develop methods for the diagnosis and therapy of neuronal dysfunction.  We aim to work in an interdisciplinary, collaborative and supportive work environment which emphasizes diversity and inclusion.

Tübingen has an internationally renowned research community in artificial intelligence, machine learning and computational neuroscience, including the Cyber Valley Initiative, the Tübingen AI Center, the Excellence Cluster Machine Learning, and the new MSc Program Machine Learning. We are situated in the AI Research Building, in close proximity to the Max Planck Institutes for Intelligent Systems and Biological Cybernetics, and participate in the two International Max Planck Research Schools (IMPRS) 'Intelligent Systems' and 'Mechanisms of Mental Function and Dysfunction'.

The position is open to candidates who have a PhD or Master’s  in in a quantitative discipline (e.g. computer science, maths, statistics, physics, electrical engineering, computational neuroscience), a genuine interest in interdisciplinary work at the interface of machine learning and neuroscience, and strong programming skills (ideally Python/PyTorch).  Prior experience in deep learning, and/or in analysing neurophysiological data with statistical methods is advantageous.

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.

PhD applicants are also expected to apply to the IMPRS 'Intelligent Systems', https://imprs.is.mpg.de.

Please submit your application materials to mls-sekretariatspam prevention@inf.uni-tuebingen.de, with subject 'Application: Postdoc/PhD DeepHumanVision'. Please include a CV, a brief statement of research interests, contact details of two referees and a work sample - anything that is genuinely your own work, e.g. a thesis, computer code, a research manuscript, an essay, or a publication. For postdoc applicants, we expect relevant prior publications.