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Stellenangebote

POSTDOCTORAL RESEARCHER IN FREE ENERGY CALCULATIONS (BERLIN)

We are seeking a talented postdoctoral fellow to work at the interface of structure-informed machine learning and

alchemical free energy calculations as part of an exciting new collaboration between Prof. Dr. Andrea Volkamer and

BIH Einstein Visiting Fellow Prof. Dr. John Chodera. This project seeks to develop an integrated framework for

utilizing both state-of-the-art machine learning approaches and free energy calculations to exploit structural data,

assay data, and scalable computing resources to predict the polypharmacology of small molecule kinase inhibitors.

The postdoc will be embedded in the Volkamer group , situated within the exciting research environment of the

Charité in Berlin, and make a few extended visits to the Chodera lab at the Memorial Sloan Kettering Cancer Center

in New York City. The postdoc will be jointly supervised by Andrea Volkamer and John Chodera, with John Chodera

making several extended visits to the Charité per year under the auspices of the BIH Einstein Visiting Fellowship.

Work will focus on the development of absolute and relative alchemical free energy calculations approaches and

their integration with structure-informed machine learning approaches for predicting ligand binding affinities,

kinase polypharmacology, and the susceptibility of inhibitor binding affinities to clinical resistance mutations. The

postdoc will focus on extending our open source GPU-accelerated Python toolkits (such as openmmtools , yank , and

perses ) for protein-ligand absolute and relative alchemical free energy calculations (built on OpenMM ) and the

development of robust modeling and prediction workflows. This is an exciting opportunity to work at the

intersection of physical modeling, machine learning, and drug discovery as a stepping stone to a career in industry

or academia. Position is pending final confirmation of funding.

Starting date: 1 Feb 2019 or shortly thereafter (for up to 3 years)

Salary: TV-L E13, 100%

Desired qualifications :

● Experience with the theory and practice of small molecule alchemical free energy calculations

● Comfortable with the Python programming language

● Exposure to modern open source software development practices (GitHub, unit tests, continuous

integration)

● Good multidisciplinary team working and communication skills

Bonus qualifications:

● Experience with high-performance computing clusters and/or cloud computing

● Experience with OpenMM or machine learning frameworks such as TensorFlow

● Experience with cheminformatics, computer-aided drug discovery and/or kinase inhibition

How to apply: Interested candidates are invited to send a pre-application to Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein! with the

subject line “Postdoc application” that includes:

● a cover letter explaining your motivation, background, and qualifications for the position

● a detailed Curriculum Vitae (including a list of publications)

● contact information of two references

Deadline: 21 Dec 2018

see also:

https://www.charite.de/service/stellenangebot/angebot/detailinfo/dm19818b_postdocresearch_scientist/

Ph.D. Student in Biomedical Data Science (HIV)

The Chair for Methods in Medical Informatics (Prof. Dr. Nico Pfeifer), Department of Computer Science at Eberhard Karls University Tübingen, one of eleven German universities distinguished as excellent under the German government’s initiative, is currently looking for a

 

Ph.D. Student in Biomedical Data Science (HIV) (E13 TV-L, 65%)

 

starting as soon as possible. The initial fixed-term contract will be for 3 years with possible extension. The position is funded by the Machine Learning Competence Center Tübingen (TUE.AI Center).

 

According to WHO about 37 million people have been living with HIV/AIDS world-wide at the end of 2017. Since there is no approved curative treatment, infected people have to take life-long anti-retroviral treatments (ART). Due to the high variability of HIV, resistant variants can emerge in patients even if they are under treatment. There are even cross-resistances between different ARTs. Therefore, there is a constant need for new targets. Highly potent and broadly neutralizing antibodies (bNAbs) have been promising candidates to fulfill this need. The goal of this Ph.D. project is to extend the work by Hake and Pfeifer to build an interpretable prediction model that can be used to give decision support for bNAbs treatment by applying and extending state-of-the-art machine learning methods.

 

The group has extensive knowledge at the interface between statistical machine learning, digital medicine, and computational biology. Nico Pfeifer is a PI in the excellence cluster “Machine Learning: New Perspectives for Science” starting in January 2019. We are developing methods that allow answering new biomedical questions (Speicher and Pfeifer 2015, Proceedings of ISMB/ECCB 2015) and optimize them in close contact with our excellent national and international biomedical partners (Carlson et al. 2016, Nature Medicine, Schoofs et al. 2016, Science, Döring et al. 2016, Retrovirology, Mendoza et al. 2018, Nature).

 

Prerequisites

The ideal candidate will have an M.Sc. or equivalent in Biomedical Data Science, Biometry, Biostatistics, Bioinformatics, Medical Informatics, Computer Science, Computational Biology or a related life science discipline. Applicants should have an interest in interdisciplinary work. Experience in data science and machine learning as well as strong programming/scripting skills (C/C++, R, Matlab, Python, JavaScript, Java) are desirable. Other relevant qualifications include:

  • Background in Statistics

  • Knowledge of the adaptive immune system (especially humoral immune response)

  • Experience with medical data (clinical data, molecular data, …)

  • Experience with high-throughput data (next-generation sequencing)

  • Databases (MySQL, NoSQL)

 

In case of equal qualification and experience, physically challenged applicants are given preference. The University of Tübingen aims at increasing the share of women in science and encourages female scientists to apply. Candidates will be officially employed by the administration of the University of Tübingen.

 

Please send your application (including motivation letter, curriculum vitae, transcripts and certificates, and contact details of two academic references) via e-mail to
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein! with the subject: Ph.D. student application Biomedical Data Science (HIV).

 

Application deadline: December 21st, 2018.

Candidates are encouraged to send their application material early since we will start reviewing applications already before the deadline.

Post-doc in Biomedical Data Science (TB)

The Chair for Methods in Medical Informatics (Prof. Dr. Nico Pfeifer), Department of Computer Science at Eberhard Karls University Tübingen, one of eleven German universities distinguished as excellent under the German government’s initiative, is currently looking for a

 

Post-doc in Biomedical Data Science (TB) (E13 TV-L, 100%)

 

starting as soon as possible. The initial fixed-term contract will be for 2 years with possible extension.

 

According to WHO there were 558,000 cases of drug-resistant tuberculosis (TB) infections world-wide in 2017 of which 82% were multidrug-resistant. In this project, which is funded through a Horizon 2020 grant by the European Commission, the successful candidate will build a prediction engine that is able to provide treatment decision support for TB-infected patients by applying and extending state-of-the-art machine learning methods. This will be in close collaboration with our Western and Eastern European partners, with a focus on multidrug-resistant strains from Eastern Europe.

 

The group has extensive knowledge at the interface between statistical machine learning, digital medicine, and computational biology. Nico is a PI in the excellence cluster “Machine Learning: New Perspectives for Science” starting in January 2019. We are developing methods that allow answering new biomedical questions (Speicher and Pfeifer 2015, Proceedings of ISMB/ECCB 2015) and optimize them in close contact with our excellent national and international biomedical partners (Carlson et al. 2016, Nature Medicine, Schoofs et al. 2016, Science, Döring et al. 2016, Retrovirology, Mendoza et al. 2018, Nature).

 

Prerequisites

The ideal candidate will have a Ph.D. or equivalent in Biomedical Data Science, Biometry, Biostatistics, Machine Learning, Bioinformatics, Medical Informatics, Computer Science, Computational Biology or a related life science discipline. Applicants should have an interest in interdisciplinary work. Proven experience in data science and machine learning as well as strong programming/scripting skills (C/C++, R, Matlab, Python, JavaScript, Java) are desirable. Other relevant qualifications include:

  • Background in Statistics

  • Proficiency in Russian language (communication with Eastern European partners)

  • Knowledge of the adaptive immune system (especially humoral immune response)

  • Experience with medical data (clinical data, molecular data, …)

  • Experience with high-throughput data (next-generation sequencing)

  • Databases (MySQL, NoSQL)

 

In case of equal qualification and experience, physically challenged applicants are given preference. The University of Tübingen aims at increasing the share of women in science and encourages female scientists to apply. Candidates will be officially employed by the administration of the University of Tübingen.

 

Please send your application (including motivation letter, curriculum vitae, transcripts and certificates, and contact details of two academic references) via e-mail to
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein! with the subject: Post-Doc application Biomedical Data Science (TB).

 

Application deadline: December 21st, 2018.

Candidates are encouraged to send their application material early since we will start reviewing applications already before the deadline.

Ph.D. Student in Biomedical Data Science (HCV)

The Chair for Methods in Medical Informatics (Prof. Dr. Nico Pfeifer), Department of Computer Science at Eberhard Karls University Tübingen, one of eleven German universities distinguished as excellent under the German government’s initiative, is currently looking for a

 

Ph.D. Student in Biomedical Data Science (HCV) (E13 TV-L, 65%)

 

starting as soon as possible. The initial fixed-term contract will be for 2 years with possible extension. The position is funded by the German Center for Infection Research (DZIF).

 

The prescription of direct-acting antiviral agents is associated with high rates of sustained virological response (SVR) in patients treated against hepatitis C virus (HCV). Treatment failure is associated with the presence of resistance-associated mutations (RAMs), which can emerge during treatment or be transmitted. The use of genotypic drug resistance tests such as geno2pheno[hcv] or geno2pheno[ngs-freq], which identify RAMs in the amino-acid sequences of the non-structural (NS) proteins NS3, NS5A, and NS5B, can guide treatment decisions. The successful candidate will use statistical methods to analyze next-generation sequencing (NGS) data and apply and extend state-of-the-art machine learning methods to further improve upon current genotypic systems.

 

The group has extensive knowledge at the interface between statistical machine learning, digital medicine, and computational biology. Nico Pfeifer is a PI in the excellence cluster “Machine Learning: New Perspectives for Science” starting in January 2019. We are developing methods that allow answering new biomedical questions (Speicher and Pfeifer 2015, Proceedings of ISMB/ECCB 2015) and optimize them in close contact with our excellent national and international biomedical partners (Carlson et al. 2016, Nature Medicine, Schoofs et al. 2016, Science, Döring et al. 2016, Retrovirology, Mendoza et al. 2018, Nature).

 

Prerequisites

The ideal candidate will have an M.Sc. or equivalent in Biomedical Data Science, Biometry, Biostatistics, Bioinformatics, Medical Informatics, Computer Science, Computational Biology or a related life science discipline. Applicants should have an interest in interdisciplinary work. Experience in data science and machine learning as well as strong programming/scripting skills (C/C++, R, Matlab, Python, JavaScript, Java) are desirable. Other relevant qualifications include:

  • Background in Statistics

  • Experience with medical data (clinical data, molecular data, …)

  • Experience with high-throughput data (next-generation sequencing)

  • Databases (MySQL, NoSQL)

Knowledge of the adaptive immune system is a plus.

 

In case of equal qualification and experience, physically challenged applicants are given preference. The University of Tübingen aims at increasing the share of women in science and encourages female scientists to apply. Candidates will be officially employed by the administration of the University of Tübingen.

 

Please send your application (including motivation letter, curriculum vitae, transcripts and certificates, and contact details of two academic references) via e-mail to
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein! with the subject: Ph.D. student application Biomedical Data Science (HCV).

 

Application deadline: December 21st, 2018.

Candidates are encouraged to send their application material early since we will start reviewing applications already before the deadline.

3 PhD Student Positions in the Computational Plant Sciences []

HE GRADUATE SCHOOL
 
The International Max Planck Research School (IMPRS) on ‘Understanding Complex Plant Traits using Computational and Evolutionary Approaches’ is a local collaboration between the Max Planck Institute for Plant Breeding Research (MPIPZ) and the Universities in Cologne and Düsseldorf (Germany). The IMPRS has been established in 2002 and offers an international PhD programme in English. Students are closely supervised through an advisory committee, receive professional training in transferable and applicable skills and are supported to share their scientific insights.
 
THE RESEARCH
 
Our common goal is to understand the molecular basis of complex traits that differ between plant species and confer novel phenotypic characteristics. Computational approaches have become an integral part of our research, reflected by a continuously growing number of related projects and groups. The advertised projects will be performed in the groups of Angela Hancock (http://www.mpipz.mpg.de/imprs-hancock), Xiangchao Gan (http://www.mpipz.mpg.de/imprs-gan) and Stefan Laurent (http://www.mpipz.mpg.de/imprs-laurent & https://www.laurentlab.org/) and applicants have the opportunity to indicate a preference in the online application process.
 
YOUR PROFILE
 
You are a highly qualified and motivated student from any nationality with an MSc (or equivalent) degree in a related subject such as bioinformatics and population/quantitative genetics. You have a proved track record of academic and research excellence and are fluent in written and spoken English. Additionally, you have experience with one or more programming languages such as Python, Java, C/C++ and R.
 
OUR OFFER
 
We provide an excellent and interdisciplinary research environment with a state-of-the-art IT infrastructure. The positions are fully funded for 3 years with possible extension. We are committed to increasing the number of individuals with disabilities in the workforce and encourage applications from such qualified individuals. We furthermore seek to increase the number of women in those areas where they are underrepresented and therefore explicitly encourage women to apply.
 
YOUR APPLICATION
 
Apply by January 4, 2019 at gradschool.mpipz.mpg.de. Only applications submitted through the online platform will be considered. Shortlisted candidates will be invited to the MPIPZ for interviews on March 18-21, 2019. Successful candidates will start their positions preferably on October 1, 2019.
 
CONTACT
 
Dr. Stephan Wagner (IMPRS co-ordinator) at Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!
Apply online via https://gradschool.mpipz.mpg.de/ and find additional information at http://www.mpipz.mpg.de/imprs/

Bioinformatician [IMB]

The Institute of Molecular Biology gGmbH

funded by the Boehringer Ingelheim Foundation

is recruiting a

Bioinformatician (m/f)

(#CFBIO15) 

The Institute of Molecular Biology gGmbH (IMB) is a Centre of Excellence for Life Sciences funded by the Boehringer Ingelheim Foundation. IMB is located at the campus of the Johannes Gutenberg University in Mainz, Germany and offers premium scientific core facilities (see www.imb-mainz.de/core-facilities). The Bioinformatics Core Facility supports IMB researchers in the computational analysis of genomic data generated in the course of research projects. We are looking for a highly motivated Bioinformatician (m/f) with enthusiasm for academic research to join our team and support a newly formed Collaborative Research Centre, CRC 1361, on the topic “Regulation of DNA Repair and Genome Stability”, funded by the German Research Foundation DFG (see https://www.imb.de/about-imb/news).

You will:

•             Join exciting CRC projects studying the mechanisms of DNA repair and genome stability

•             Participate in the experimental planning, processing, interpretation and publication of various genomic, epigenomic and transcriptomic next-generation sequencing (NGS) datasets

•             Develop and implement custom bioinformatics workflows for data analysis and integration

 

Required qualifications:

•             University degree or postgraduate training in computational biology / bioinformatics

•             Extensive and broad experience in the computational analysis of NGS datasets

•             Solid programming skills and familiarity with Linux compute clusters

•             Sound knowledge of statistics, version control and R / Bioconductor

•             Good organisational, communication and interpersonal skills

•             Proficiency in written and spoken English

 

Desirable qualifications:

•             Experience in interdisciplinary collaborations or bioinformatics services

•             Knowledge of software engineering and bioinformatics pipelines

 

We offer:

•             Stimulating, diverse and international research environment

•             Flexible working hours and advanced training opportunities

•             Competitive salary and favourable pension scheme

 

To apply, please send a single PDF file containing your cover letter, CV, certificates and contact information of at least two professional references quoting Ref. No. CFBIO15 to Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!. IMB is an equal opportunity employer.

 

Starting Date:  as soon as possible

Duration:   initial contract for four years with the possibility of extension

Application Deadline:  20th January 2019

 

Declaration of Consent and Data Protection

By sending us your application, you are consenting to us saving your personal data in order to carry out the selection process. You can find more information on data protection and retention periods at www.imb.de/jobs/Data Protection.

 Links: https://www.imb.de/jobs/scientific-positions/

 

 

Wissenschaftliche/n Mitarbeiter/in (m/w/d) [Lehrstuhl für Epidemiologie/UNIKA-T]

Die Ludwig-Maximilians-Universität München ist eine der größten und renommiertesten Universitäten Deutschlands. Der Lehrstuhl für Epidemiologie/UNIKA-T, angesiedelt am Klinikum Augsburg, sucht zum nächstmöglichen Zeitpunkt eine/n

 

 

Wissenschaftliche/n Mitarbeiter/in (m/w/d)

 

 

Ihre Aufgaben:

·           Eigenständige Durchführung wissenschaftlicher Projekte zu Einflussfaktoren auf die Entstehung und den Verlauf von chronischen Krankheiten

·           Statistische Auswertung von großen Datensätzen (inklusive Omics-Daten)

·           Erstellung von wissenschaftlichen Publikationen und Berichten

·           Mitarbeit bei laufenden epidemiologischen Studien des Lehrstuhls

 

Ihr Profil:

·           Erfolgreich abgeschlossenes Hochschulstudium in den Bereichen Naturwissenschaft, Medizin oder verwandten Gebieten

·           Erfolgreich abgeschlossenes Zusatzstudium im Bereich Epidemiologie/Public Health

·           Praktische Erfahrungen in statistischer Datenauswertung (z.B. mit R oder SAS) und im Verfassen wissenschaftlicher Texte sind von Vorteil

·           Sehr gute Englischkenntnisse werden ebenso vorausgesetzt wie Kommunikationsfähigkeit, Teamgeist und Zuverlässigkeit

 

 

Unser Angebot:

Ihr Arbeitsplatz befindet sich am Campus des Klinikums Augsburg und ist gut mit öffentlichen Verkehrsmitteln zu erreichen. Wir bieten Ihnen einen interessanten und verantwortungsvollen Arbeitsplatz mit guten Weiterbildungs- und Entwicklungsmöglichkeiten.

 

Die Anstellung erfolgt befristet für 3 Jahre. Die Eingruppierung erfolgt nach dem TV-L, E13 (65%). Es besteht die Möglichkeit zur Promotion. Schwerbehinderte Bewerber/innen werden bei ansonsten im Wesentlichen gleicher Eignung bevorzugt. Die Bewerbung von Frauen wird begrüßt.

 

Bitte schicken Sie Ihre aussagekräftigen Bewerbungsunterlagen bis spätestens 07.01.2019 per Email an das Sekretariat des Lehrstuhls für Epidemiologie der LMU München am UNIKA-T, Frau Kötzner, Email: Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!. Für dringende Rückfragen steht Ihnen die Lehrstuhlleitung unter der Telefonnummer 0821-598-6471 zur Verfügung.

PostDoc Position in Network Epigenomics

Full time position for a PostDoc to fill in the newly established group for Big Data in Biomedicine at the Chair of Experimental Bioinformatics, Technical University of Munich, Freising, Germany.
 
Project description
A major research barrier in systems biomedicine is that most of the currently used gene-gene interaction networks are not cell type-specific and neglect much of our current understanding of cellular regulation (i.e. enhancers and repressors). The EpiMap project of the International Human Epigenome Consortium currently integrates and harmonizes multi-omics data from 1000 epigenomes, offering a unique chance to overcome this barrier. The aim of this research project is to use state-of-the-art machine learning techniques for multitask and transfer learning to construct robust cell-type-specific molecular interaction networks. Such networks will allow researchers to put their experimental results in a cell type-specific context and thus pave the way towards adding an epigenetic perspective to biomedical research in general and to systems medicine in particular.
 
Your qualifications
- Recent PhD in Computational Biology, Bioinformatics, Genomics, Epigenetics or a related field. 
- Strong experience with handling RNA-seq, small RNA-seq, WGBS, RRBS, ChiP-seq data 
- Strong experience with systems biology and network analysis techniques
- Strong experience with advanced machine learning techniques, e.g. SVNs, RF, CNN
- Strong experience in R, Python or Java
- Good experience in using compute clusters / HPC environments and Docker / Singularity
- Solid understanding of molecular biology in general and epigenetic mechanisms in particular
- Ability to independently carry out a challenging research project in an international collaboration
- Fluency in English in written and spoken language.
 
What we offer
At the Chair of Experimental Bioinformatics you will find a supportive and productive research environment with a young, dynamic team of more than 20 international researchers at different stages in their career and education. Find us online at: https://www.exbio.de and https://biomedical-big-data.de.
 
The position is available starting immediately and initially limited for 3 year with a gross salary calculated according to A13 TV-L.
 
Interested? Send your application including your CV, the most relevant publications and two reference letters to Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!.
 
Links: URL for this position announcement: https://biomedical-big-data.de/post/job-announcement-nov18/

Horizontal functional inference to leverage the metagenomic data deluge

The Soeding lab (https://www.mpibpc.mpg.de/soeding) at the MPI for biophysical chemistry in Goettingen is looking to fill a positions for postdocs (E13 TVoeD Bund, three years), starting ~ 01/2019 or later, in methods development for metagenomics. 
 

Motivation: To predict the function of protein sequences in metagenomes, all common tools search for related sequences in the reference databases, from which the functional annotation can be inferred. But many species found in metagenomics studies are not closely related to any organism with a well-annotated genome. Therefore, the fraction of protein sequences in metagenomic data that cannot be annotated using this "vertical" information transfer is often as high as 65% to 90%.  This is the major obstacle to make progress.

Project: We want to develop a new paradigm for function prediction based on the transfer of contextual, ”horizontal” information. Building on our MMseqs2 software for fast sequence and profile searches [1] and sequence clustering [2], we will develop a very fast sequence search method that can find clusters of neighboring and co-transcribed genes. The basis idea of utilising genomic context is similar to well-known tools such as the STRING database. However, we are devising a novel statistical approach which, in combination with MMseqs2 and Linclust, will allow us to analyse huge numbers of genomes and metagenomes.  Using iterative profile searches combined with horizontal information transfer, we will mine massive amounts of genomic and metagenomic data to learn functional modules of genes / proteins that will subsequently be used for improved annotation. This novel approach promises to greatly accelerate the rate of biological and biotechnological discoveries by deep mining of metagenomic and genomic sequence data. 

Your profile: The project requires very good programming skills and interest in writing efficient, fast code. Experience with C++ and AVX2 is not required and can be learned on the job. Furthermore, an interest and some background in statistical approaches in bioinformatics will be highly useful. Women are particularly encouraged to apply. Applications from non-germans are very welcome. 
 
Our group: Our group of 3 postdocs, 6 PhD students, two master students and a PI develop statistical and computational methods for analyzing data from high-throughput biological experiments. You would join a team of 3 PhD students and a postdoc working on method development for metagenomics.

If you are interested I would be happy to hear from you!
Johannes Soeding <Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!>
 
 
[1] Steinegger, M., and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnol. 35, 1026–1028.  https://doi.org/10.1038/nbt.3988
 
[2] Steinegger, M., and Söding, J. (2018) Clustering huge protein sequence sets in linear time. Nature Commun. 9, 2542.  https://doi.org/10.1101/104034
 
 
 

 

 

PhD Positions in Computational Cancer Research in Heidelberg

Are you interested in solving fascinating biological or medical questions computationally?

 

At the German Cancer Research Center (DKFZ) in Heidelberg, Germany’s largest biomedical research institute, computational scientists work at the forefront of cancer research. They combine inter­disciplinary approaches from physics, biology, bioinformatics and statistics to analyze and understand complex biological and medical data.

 

Main activities at the DKFZ involving big data:

•             Development and application of computa­tional methods for the integrative analysis of genomic, epigenomic, transcriptomic, proteomic, meta­bolomic and radiomic data

•             Development of methods for modeling and simulation of biological and medical processes

•             DNA mutation analysis by next-generation DNA sequencing of individual human cancer genomes

•             Application of state-of-the-art technologies in automated live-cell imaging and image analysis

•             Computer-assisted radiology and surgery

•             Biostatistical evaluation of experimental and clinical data

•             Integration and development of new methods for systems biology and systems medicine

 

More information about computational research at the DKFZ can be found at:

https://www.dkfz.de/en/forschung/schwerpunkte/fsp-e.php?campaign=phd/bioinf

and

https://www.dkfz.de/en/forschung/schwerpunkte/fsp-b.php?campaign=phd/bioinf

 

Full funding is provided for the duration of the PhD.

 

If you are interested in doing your PhD in the field of computational cancer research, why not join the International PhD Program at the DKFZ with more than 500 PhD students?

More information about the program at http://www.dkfz.de/en/phd-program/index.html?campaign=phd/bioinf

 

 

Application deadline: 5th January 2019