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Stellenangebote

Postdoc Position: Bioinformatics of Long Noncoding RNA Functional Elements

The “Genomics of Long Non-Coding RNAs in Disease” Laboratory (GOLD Lab) at the University of Bern has an opportunity for a Bioinformatics Postdoc.

The Project: Understanding how long noncoding RNA (lncRNA) functions are encoded in their sequence is a great challenge in biology. Our goal is to discover, classify and characterise lncRNA functional elements by means of an integrative bioinformatic / experimental approach. You will lead the bioinformatic component to develop novel methods for identifying lncRNA elements and predicting their functions. In collaboration our experimental team, you will have ample opportunities for experimental validation of in silico-generated hypotheses.

The Group: We are an international and interdisciplinary group of researchers with a passion for lncRNA research. We foster an open and collaborative working environment. Our work is funded by the Swiss National Center for Competence in Research (NCCR) “RNA & Disease” (nccr-rna-and-disease.ch), and we participate in the GENCODE project and International Cancer Genome Consortium (ICGC). We also have excellent biomedical links at the University Hospital of Bern. For more information about us: gold-lab.org / twitter.com/goldlab_bern

The City: Bern, the capital of Switzerland, has a vibrant international community, numerous outdoors and cultural activities, and ranks among the best cities worldwide for quality of life (https://tinyurl.com/y9vrtb7d).

The Person: We seek a dedicated and dynamic colleague to integrate into our diverse team. You should have strong background in bioinformatics or computer science, and specifically some/all of: Unix environment, R, perl/python, analysis of NGS data, webservers/databases. Experience in lncRNAs, CRISPR-Cas9 is a plus.

Details: Ideal start date will be Q1 of 2019. Project is fully funded for 4 years. Salary is according to (generous) University of Bern scales.

Recent publications:

Carlevaro-Fita et al. Biorxiv https://www.biorxiv.org/content/early/2017/10/23/189753

Uszczynska-Ratajczak B et al. Nat Rev Genet. 2018 Sep;19(9):535-548. doi: 10.1038/s41576-018-0017-y.

Lagarde J et al. Nat Genet. 2017 Dec;49(12):1731-1740. doi: 10.1038/ng.3988.

To apply: Please send a motivation letter, publications list, list of references, and CV to Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein! with subject title: “Job application Bioinformatics Postdoc”. Informal enquiries are also welcome.

W3-Professur für Bioinformatik [Heidelberg]

Am Institut für Pharmazie und Molekulare Biotechnologie (IPMB) der Universität Heidelberg ist eine

W3-Professur für Bioinformatik

zu besetzen.

Die Einbettung der Professur in das IPMB eröffnet die Möglichkeit, bioinformatische Methoden unmittelbar auf experimentellen, biologisch-therapeutischen und biotechnologischen Gebieten anzuwenden. Aufgrund der führenden Position der Universität Heidelberg und der benachbarten biomedizinischen Forschungseinrichtungen bestehen für die Professur darüber hinaus hervorragende Vernetzungs- und Entwicklungsmöglichkeiten im translational-bioinformatischen Bereich. Von der/dem Stelleninhaber/in werden ausgewiesene Forschungskompetenzen in der Bioinformatik mit Fokus auf Anwendungen in der Pharmakogenomik, der personalisierten Medizin oder Biotechnologie erwartet.

Von der/dem Stelleninhaber/in wird die Fähigkeit und Bereitschaft erwartet, die Lehrveranstaltungen in den Fächern Bioinformatik und Mathematik für die Bachelor- und Masterstudiengänge Molekulare Biotechnologie und den Staatsexamens-Studiengang Pharmazie persönlich abzuhalten. Der/die Stelleninhaber/in gestaltet die Lehre im Schwerpunkt Bioinformatik des BSc Studiengangs Molekulare Biotechnologie.

Die Einbindung der Professur in die vom IPMB verantworteten Studiengänge eröffnet die Möglichkeit zur forschungsorientierten Lehre, welche in den letzten Jahren beispielsweise durch die Erfolge im iGEM Wettbewerb eine erhebliche Sichtbarkeit erzielt hat.

Voraussetzung für die Bewerbung ist ein abgeschlossenes Hochschulstudium, sowie nach § 47 Abs. 2 Landeshochschulgesetz die Habilitation, die erfolgreich evaluierte Juniorprofessur oder eine vergleichbare Qualifikation, insbesondere die Leitung einer unabhängig evaluierten Nachwuchsgruppe. Die Universität Heidelberg strebt einen höheren Anteil von Frauen in den Bereichen, in denen sie unterrepräsentiert sind, an. Qualifizierte Wissenschaftlerinnen werden daher besonders um ihre Bewerbung gebeten. Schwerbehinderte werden bei gleicher Eignung bevorzugt.

Bewerbungen mit Lebenslauf, wissenschaftlichem Werdegang, Schriftenverzeichnis, Listen der bisherigen und laufenden Drittmittelprojekte und Lehrveranstaltungen, sowie einer kurzen Darstellung der zukünftigen Forschungsplanung werden, bevorzugt in elektronischer Form, zusammengefasst in einem einzelnen pdf-Dokument, bis zum 04.01.2019 erbeten an:
Frau Prof. Dr. Karin Schumacher, Dekanin der Fakultät für Biowissenschaften, Im Neuenheimer Feld 234, 69120 Heidelberg, e-mail:
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!, unter dem Betreff: „Bioinformatik 2019“. 

Wir bitten um Verständnis, dass eingegangene Bewerbungsunterlagen nicht zurückgesandt werden.

Weitere Informationen zum Institut für Pharmazie und Molekulare Biotechnologie (IPMB) sind zu finden unter: https://www.ipmb.uni-heidelberg.de/ 

PhD and Post-doc position in Bioinformatics [Meyer lab]


(1) Post-doc position in RNA structure and transcriptome Bioinformatics at the BIMSB-MDC in Berlin, Germany

application deadline: Friday, 30. November 2018

for details see

https://www.mdc-berlin.de/career/jobs/post-doc-position-bioinformatics

and the Meyer lab web page

https://www.mdc-berlin.de/meyer#t-news

(2) PhD position in RNA structure Bioinformatics at the BIMSB-MDC in Berlin, Germany

application deadline: Friday, 30. November 2018

for details see

https://www.mdc-berlin.de/career/jobs/phd-position-bioinformatics

and the Meyer lab web page

https://www.mdc-berlin.de/meyer#t-news

Bioinformatician for translational cancer research (Saur lab)

The Klinikum rechts der Isar university hospital of the Technical University of Munich is a major European center of medical care and education. With over 1.161 beds and almost 5.500 employees, we treat 300.000 patients per year with state-of-the art care.

The “Department of Medicine II” of the Technical University of Munich (TUM) and the Institute of Translational Cancer Research at TUM and the German Cancer Research Center invites applications for a PhD Student/Post-doctoral fellow or Bioinformatician staff position in Bioinformatics and Cancer Biology at the Center for Translational Cancer Research (TranslaTUM) in Munich.

We are deploying genetic models to study molecular, immunological and translational aspects of pancreatic cancer. We utilize these systems to uncover molecular mechanisms as well as inflammatory and immune pathways that are required for tumor initiation and -progression in vivo (e.g. Nature 2018, 554:62-68; Nat. Commun. 2016, 7:10770; Nat Med. 2014, 20:1340-7; Cancer Cell. 2013,24:15-29, Cancer Cell. 2013,23:406-20, Nat Commun. 2013;4:1630; PNAS 2011, 108:9945-50). During the past decade, our lab has isolated a cohort of thousand PDAC primary cell cultures. Molecular characterization with multiple NGS methods has been performed. The advertised project is therefore focused on unraveling the molecular mechanisms of tumor development by analyzing the before mentioned annotated resources.

We are a young and enthusiastic team and offer intensive training and mentoring. The successful candidate will have the opportunity to:

- Set up a bioinformatics platform and develop in-house computational pipelines for analysis of omics datasets, with particular focus on Next-Generation DNA Sequencing (NGS) platform for Whole Exome Sequencing, Whole Genome Sequencing and RNA-seq.

- Utilize available software and pipelines to generate and analyze NGS datasets from DNA- and RNA-based experiments;

- Apply bioinformatics techniques to published/unpublished data for the analysis and interpretation of omics datasets;

- Aid researchers select and use computational and statistical tools relevant to their projects.

 

The candidate will interact within a multidisciplinary environment of biologists, clinicians, computer scientists and mathematicians within the TranslaTUM. The successful candidate will work closely with a diverse team to formulate hypotheses and validate predictions using genomic assays. She/he will be exposed to cutting edge methods to study translational aspects of cancer (e.g. Cre/loxP and Flp/frt based dual- and triple recombinase systems, organoid culture, orthotopic transplantations of organoids, CRISPR/Cas9, in vivo imaging, in vivo transposon mutagenesis).

We are looking for a computational scientist with good record at the PhD or experienced MSc level. Previous exposure to and experience with bioinformatics as well interests in molecular aspects of cancer research is essential.

Payment is according to tarif (TV-L). We give priority to severely disabled applicants with essentially equal qualifications.

Please send your application including a letter of motivation, CV, certificates, recommendation letters in one pdf file to Prof. Dieter Saur (e-mail: Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!).]

Links: https://www.translatum.tum.de/

PhD candidate position in Data Science/ Bioinformatics [Uni Basel]

A PhD candidate position is available at the Transfaculty Research Platform Molecular and Cognitive Neuroscience (MCN, www.mcn.unibas.ch/) of the University of Basel. MCN focuses on the neurobiological underpinnings of human behaviour and on the development of novel treatment options for neuropsychiatric disorders. Our research is enabled by our in-house computational platform which provides access to multi-omic data sets from preclinical data, clinical trials as well as in-house and public genomics and brain imaging data.
The research project will focus on extending and utilizing our existing computational analytics platform for reproducible research. The successful candidate will collaborate with bioinformaticians, statisticians, psychologists, and biologists to identify critical research questions and address them via computational approaches.‬ ‬‬‬‬‬‬‬‬‬
The successful candidate will have a Master's degree (or comparable) and a strong background in Data Sciences, Bioinformatics, Computer Science, Statistical Genetics, Neuroscience or related disciplines. The ideal candidate will have demonstrated hands-on experience of programming in R and SQL, experience in using git and Linux as well as affinity to statistical (genetics) methods. While English language skills are essential, German language skills are useful but not essential.
We offer the opportunity to work, study, and do research in an excellent, highly interdisciplinary scientific environment supported by research group leaders and staff. The Basel area is a sunny and cosmopolitan region in the Northwest of Switzerland right next to France and Germany and the University of Basel offers internationally competitive salaries.
Applications should be submitted electronically including your CV, research experience and the names of two references to Dr. Thomas Schlitt, Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!
www.unibas.ch

PhD position in Machine Learning for Genomics [Gagneur lab]

A PhD position is available in the Computational Biology group of the Technical University of Munich (Prof. Julien Gagneur) starting immediately.

 

Your role
You will develop computational methods and models expanding Kipoi, a collaborative initiative to define standards and to foster reuse of trained models in genomics [1]. Kipoi builds on a 3-way collaboration with international partners (Stegle lab, EMBL Heidelberg, and Kundaje lab, Stanford). The Kipoi model repository at https://kipoi.org is increasingly used and extended by the research community.

 

Research topics include: expressive mathematical representations of RNA or protein encoded regulatory sequences using deep learning approaches (e.g. [2]); integrative models of individual steps of gene expression; methodologies for interpretability of deep learning models, and for their application to the prediction of causal effects of genetic variants in rare or common diseases (e.g. [3]). We expect applications on large-scale public data and on unpublished datasets from experimental collaborators in biology (e.g. [4]) or medicine (e.g. [5]).

 

You are
Applicants must hold a master in bioinformatics, or in physics, statistics, or applied mathematics with a genuine interest in applications to genomics. (S)he should have know-how in machine learning or statistical modeling and demonstrated programming experience with R or python. (S)he should have excellent communications skills and work within an interdisciplinary setting.

 

We are
The Gagneur lab is a lively, international, and interdisciplinary computational biology group with a research focus on the genetic basis of gene regulation and its implication in diseases. We are located in the informatics department of the Technical University of Munich, one of the top ranked European universities. Our lab has strong links to other local scientists and institutions in biology and medicine. Munich offers an outstanding, dynamic, interactive and internationally oriented research environment. Munich, the 2018 “most livable city in the world” according to the urban magazine Monocle, and the proximity of the Alps provide an excellent quality of life.

 

Apply
The position is funded from core funding with a salary according to the TV-L (German academic salary scale). We encourage joining the graduate school QBM (Quantitative Bioscience Munich). Applications including a cover letter, CV, and references must be sent to Julien Gagneur (Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!, cc: Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!) until Nov 30th 2018 referring to “PhD-Kipoi18” in the title.

 

More

 

1. Avsec et al., Kipoi: accelerating the community exchange and reuse of predictive models for genomics, bioRxiv, 2018
2. Avsec et al., Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks, Bioinformatics, 2017
3. Cheng et al., Modular modeling improves the predictions of genetic variant effects on splicing, bioRxiv, 2018 – winner model of the CAGI 2018 splicing challenge
4. Schwalb et al., TT-seq maps the human transient transcriptome, Science, 2016
5. Kremer et al., Genetic diagnosis of Mendelian disorders via RNA sequencing, Nature communs, 2017

 

 

3-year Post-doc position in Machine Learning for Genomics [Gagneur lab]

Your role
You will develop computational methods and models expanding Kipoi, a collaborative initiative to define standards and to foster reuse of trained models in genomics [1]. Kipoi builds on a 3-way collaboration with international partners (Stegle lab, EMBL Heidelberg, and Kundaje lab, Stanford). The Kipoi model repository at https://kipoi.org is increasingly used and extended by the research community.

The position is funded via the recently awarded research network MechML. Research topics include: expressive mathematical representations of RNA or protein encoded regulatory sequences, notably using deep learning approaches (e.g. [2]); development of integrative models of individual steps of gene expression; development of methodologies for interpretability of deep learning models, and for their application to the prediction of causal effects of genetic variants in rare or common diseases (e.g. [3]). We expect applications on large-scale public data and on unpublished datasets from experimental collaborators in biology (e.g. [4]) or medicine (e.g. [5]).

You are
Applicants must either hold a PhD in bioinformatics, or hold a PhD in physics, statistics, or applied mathematics with practical experience with deep learning methods and application to real world high-dimensional data. (S)he must have a proven publication record, interest for translational research, and can work independently and creatively. (S)he should have excellent communications skills and be able to articulate clearly the scientific and technical needs, set clear goals and work within an interdisciplinary setting.

We are
The Gagneur lab is a lively, international, and interdisciplinary computational biology group with a research focus on the genetic basis of gene regulation and its implication in diseases. We are located in the informatics department of the Technical University of Munich, one of the top ranked European universities. Our lab has strong links to other local scientists and institutions in biology and medicine. Munich offers an outstanding, dynamic, interactive and internationally oriented research environment. Munich, the 2018 “most livable city in the world” according to the urban magazine Monocle, and the proximity of the Alps provide an excellent quality of life.

Apply
The position is funded for three years with a salary according to the TV-L (German academic salary scale).

Applications including a cover letter, CV, and references must be sent to Julien Gagneur (Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!, cc: Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!) until Nov 30th 2018 referring to “Postdoc-mechML” in the title.

More

1. Avsec et al., Kipoi: accelerating the community exchange and reuse of predictive models for genomics, bioRxiv, 2018
2. Avsec et al., Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks, Bioinformatics, 2017
3. Cheng et al., Modular modeling improves the predictions of genetic variant effects on splicing, bioRxiv, 2018 – winner model of the CAGI 2018 splicing challenge
4. Schwalb et al., TT-seq maps the human transient transcriptome, Science, 2016
5. Kremer et al., Genetic diagnosis of Mendelian disorders via RNA sequencing, Nature communs, 2017
 

PhD position in Computational Biology for Genetic Disorders [Gagneur lab]

A PhD position is available in the Computational Biology group of the Technical University of Munich (Prof. Julien Gagneur) starting as soon as possible.

Your role
You will develop computational methods and analyse multi-omics datasets (genome, transcriptome, proteome, metabolome) to unravel the genetic and molecular basis of genetic disorders. Your research topics include: Detection of aberrant molecular events in multi-omics dataset, generalizing our software OUTRIDER for RNA-seq to multiple omics data modalities [1]; development of genetic variant and gene prioritization algorithms by integrating multi-omics data together with deep learning models of regulatory variants, leveraging the model repository of machine learning models for genomics Kipoi (https://kipoi.org, [2]); Integration of multi-omics with wearable sensor data in the context of a new research network with Stanford (Lars Steinmetz lab). You will work directly on patient data, in tight collaboration with two close collaborators: Dr. Holger Prokisch, who is coordinating the European consortium for mitochondrial disorder GENOMIT (example collaboration [3]), and Prof. Christoph Klein, head of the Children’s Hospital of the University of Munich (example collaboration [4]).

You are
Applicants must hold a master in bioinformatics, or in physics, statistics, or applied mathematics with a genuine interest in applications to genomics. We expect know-how in machine learning or statistical modeling and demonstrated programming experience with R or python.  (S)he should have excellent communications skills and be able to articulate clearly the scientific and technical needs, set clear goals and work within an interdisciplinary setting (biologists and geneticists).

We are
The Gagneur lab is a lively, international, and interdisciplinary computational biology group with a research focus on the genetic basis of gene regulation and its implication in diseases. Our group is located in the informatics department of the Technical University of Munich, one of the top ranked European universities. Our lab has strong links to other local scientists and institutions in biology and medicine. Munich offers an outstanding, dynamic, interactive and internationally oriented research environment. Munich, the 2018 “most livable city in the world” according to the urban magazine Monocle, and the proximity of the Alps provide an excellent quality of life.

Apply
The position is funded from core funding with a salary according to the TV-L (German academic salary scale). We encourage joining the graduate school QBM (Quantitative Bioscience Munich). 

Applications including a cover letter, CV, and references must be sent to Julien Gagneur (Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!, cc: Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!) until Nov 30th 2018 referring to “PhD-rare18” in the title.

More
https://www.gagneurlab.in.tum.de
https://kipoi.org
https://qbm.genzentrum.lmu.de
1. Brechtmann et al., OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data, AJHG, in press and bioRxiv
2. Avsec et al., Kipoi: accelerating the community exchange and reuse of predictive models for genomics, bioRxiv, 2018
3. Kremer et al., Genetic diagnosis of Mendelian disorders via RNA sequencing. Nature communs, 2017
4. Witzel et al., Chromatin remodelling factor SMARCD2 regulates transcriptional networks controlling early and late differentiation of neutrophil granulocytes, Nature Genetics, 2017

Post-doc position in Computational Biology for Genetic Disorders [Gagneur lab]

Your role
You will develop computational methods and analyse multi-omics datasets (genome, transcriptome, proteome, metabolome) to unravel the genetic and molecular basis of genetic disorders. Your research topics include: Detection of aberrant molecular events in multi-omics dataset, generalizing our software OUTRIDER for RNA-seq to multiple omics data modalities [1]; development of variant and gene prioritization algorithms by integrating multi-omics data together with deep learning models of regulatory variants, leveraging the model repository of machine learning models for genomics Kipoi (https://kipoi.org, [2]); Integration of multi-omics with wearable sensor data in the context of a new research network with Stanford (Lars Steinmetz lab). You will work directly on patient data, in tight collaboration with two close collaborators: Dr. Holger Prokisch, who is coordinating the European consortium for mitochondrial disorder GENOMIT (example collaboration [3]), and Prof. Christoph Klein, head of the Children’s Hospital of the University of Munich (example collaboration [4]).

 

You are
Applicants must either hold a PhD in computational biology, or hold a PhD in physics, statistics, or applied mathematics with practical experience with real world high-dimensional data analysis. Applicants with a PhD in biology with strong quantitative skills and demonstrated  experience with genetics and analysis of sequencing data will also be considered. The candidate must have a proven publication record, interest for translational research, and have demonstrated the ability to work independently and creatively. (S)he should have excellent communications skills and be able to articulate clearly the scientific and technical needs, set clear goals and work within an interdisciplinary setting, communicating with biologists and geneticists.

 

We are
The Gagneur lab is a lively, international, and interdisciplinary computational biology group with a research focus on the genetic basis of gene regulation and its implication in diseases. We are located in the informatics department of the Technical University of Munich, one of the top ranked European universities. Our lab has strong links to other local scientists and institutions in biology and medicine. Munich offers an outstanding, dynamic, interactive and internationally oriented research environment. Munich, the 2018 “most livable city in the world” according to the urban magazine Monocle, and the proximity of the Alps provide an excellent quality of life.

 

Apply
The position is funded from core funding with a salary according to the TV-L (German academic salary scale).

 

Applications including a cover letter, CV, and references must be sent to Julien Gagneur (Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!, cc: Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!) until Nov 30th 2018 referring to “Postdoc-rare18” in the title.

 

More

1. Brechtmann et al., OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data, AJHG, in press and bioRxiv

2. Avsec et al., Kipoi: accelerating the community exchange and reuse of predictive models for genomics, bioRxiv, 2018
3. Kremer et al., Genetic diagnosis of Mendelian disorders via RNA sequencing. Nature communications, 2017

 

4. Witzel et al., Chromatin remodelling factor SMARCD2 regulates transcriptional networks controlling early and late differentiation of neutrophil granulocytes, Nature Genetics, 2017

Bioinformatics position at University of Cologne

The Collaborative Research Center Predictability in Evolution is a leading consortium in experimental and theoretical studies of evolutionary processes. We focus on fast evolution in microbial, viral, cancer, and immune systems, which have a wide range of biomedical applications. At University of Cologne (Germany) and its partner institutions, the DFG-funded Center unites a strong and interdisciplinary spectrum of competence in molecular genetics, biophysics, medicine, and theoretical modelling. All members take full benefits of the Center’s joint research and training facilities.

We are looking for an excellent computational biologist to play an integral part in the science of our Center. If you enjoy bringing top-notch computational analysis to exciting projects, to play an active part in planning and analysis of experiments and modelling, and to discuss your results in a vibrant community, this position is for you. Specifically, you will develop powerful project-specific analysis for high-throughput data (e.g. deep sequencing data), train other scientists in using those methods, and implement user-friendly interfaces for broader use of new algorithms developed in the consortium. You should have a doctoral degree in a relevant field and a track record demonstrating programming skills and experience with the analysis of molecular high-throughput data. Experience with the programming of user interfaces is also welcome. The salary is comparable to a post-doctoral scientist.

Applications and enquiries should be directed to Christa Stitz (Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!). Applications should include a CV, a list of publications, and other relevant credentials. Two letters of recommendation should be sent independently. The call is open until a position is filled; preferential consideration will be given to applications received before December 1st, 2018.

The University of Cologne and its partner institutions are equal opportunity employers. Applicants with disabilities will be employed with preference, given equal qualification and capability. Applications from women are explicitly encouraged and will be given particular consideration.