TECHNISCHE UNIVERSITAT DRESDEN (TU DRESDEN)

Research Associate Computational Biology

Location
Dresden, Sachsen (DE)
Salary
according to salary group E 13 TV-L
Posted
16 Jul 2020
End of advertisement period
07 Aug 2020
Academic Discipline
Life sciences, Biological Sciences
Contract Type
Fixed Term
Hours
Full Time

At TU Dresden, Center for Molecular and Cellular Bioengineering (CMCB), Biotechnology Centre (BIOTEC), the Junior Research Group “Biomedical Genomics” offers a position as

Research Associate Computational Biology

(Subject to personal qualification employees are remunerated according to salary group E 13 TV-L)

for 2 years, starting as soon as possible. The period of employment is governed by § 2 (1) Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz - WissZeitVG). The position offers the chance to obtain further academic qualification.

The Junior Research Group “Biomedical Genomics” is headed by Dr. Anna Poetsch. We focus on understanding the mechanisms behind DNA damage, repair and mutagenesis, in particular those mechanisms that lead to heterogeneous distributions of damage and/or mutations over the genome. While we focus on computational techniques on site, we collaborate closely with wet-lab molecular biologists and practicing physicians with the purpose of applying our research to a deeper understanding of basic molecular biology, and also to address current challenges in cancer treatment. 

Currently we focus on 1) investigating mutagenic mechanisms and their specificity using machine learning 2) understanding the complexity of gene regulation during the DNA damage response using cancer genomics and functional genomics techniques (RNA-Seq, ChIP-Seq, iCLIP, GRO-Seq, etc.), and 3) what these mechanisms mean for precision, specificity, and safety of genome editing. 

For more information visit http://digs-bb.de/poetsch

Project Summary:

We have found that oxidative DNA damage and related mutations are distributed heterogeneously over the genome, which has been described for multiple mechanisms that lead to mutagenesis. However, a comprehensive view over multiple mechanisms in their interaction is lacking and the mechanisms that lead to heterogeneous distribution are currently still poorly explained.

Using machine learning and deep learning data analysis techniques, the post holder will investigate the behaviour of mutagenic mechanisms and interrogate the mechanistic basis for their specificity in the genome.

The successful candidate will be expected to also collaborate closely with other members of the Biomedical Genomics group as well as clinical and/or molecular biological collaborating groups.

Tasks:

  • Lead specific research project;
  • Perform expert analysis of cancer genomics and functional genomics data;
  • Develop and apply machine learning techniques to these kinds of data;
  • Contribute to collaborative projects within the lab and with collaborating labs;
  • Assist with organisational tasks;
  • Assist with supervision of junior lab members;
  • Participate and contribute to lab meetings;
  • Lead and contribute to the preparation of scientific manuscripts.

Requirements:

The post holder should be eager to perform science in an interdisciplinary, collaborative and happy lab, in addition to the following:

• university and PhD degree in a relevant subject with an extensive analytical component e.g. bioinformatics, cancer genomics, statistics, molecular biology or mathematics or other areas relevant to computational biology;

  • An understanding of programming in a higher-level language (R/C/C++/Python), particularly with regard to big data, data visualisation and machine learning techniques;
  • Experience in bioinformatics or a related biological field, with applying statistical techniques to biological data;
  • A basic knowledge of genomics and/or general molecular biology;
  • A broad comprehension of high throughput genomic technologies;
  • Demonstrable experience of cancer genomics and functional genomics analysis methodologies data analysis will be an asset;
  • Excellent communication skills within an interdisciplinary research environment;
  • Excellent scientific analysis skills;
  • Excellent oral and writing skills in the English language (German is not required).

We are looking for a highly motivated computational biologist. The successful candidate will use machine learning to investigate genome specificity behind mechanisms of DNA damage, repair and mutagenesis. This exciting opportunity is embedded in an interdisciplinary group with the local expertise ranging from biochemistry via functional genomics to machine learning.

We offer:

  • Training and commitment for further career development
  • Friendly and collaborative work environment
  • Possibilities for interdisciplinary collaboration.

Applications from women are particularly welcome. The same applies to people with disabilities.

Please submit your comprehensive application including a detailed CV, bibliography, cover letter and the name of two references by 07.08.2020 (stamped arrival date applies) preferably via the TU Dresden SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf document to anna.poetsch@tu-dresden.de or by mail to: TU Dresden, BIOTEC, Nachwuchsforschungsgruppe Biomedizinische Genomik, Frau Dr. Anna Poetsch, Tatzberg 47/49, 01307 Dresden. Please submit copies only, as your application will not be returned to you. Expenses incurred in attending interviews cannot be reimbursed.

Reference to data protection: Your data protection rights, the purpose for which your data will be processed, as well as further information about data protection is available to you on the website: https: //tu-dresden.de/karriere/datenschutzhinweis

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