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Deep learning expert (Postdoc or similar)
Deep learning expert (Postdoc or similar)
The Centre for Genomic Regulation (CRG) is an international biomedical research institute of excellence, based in Barcelona, Spain, with more than 400 scientists from 44 countries. The CRG is composed by an interdisciplinary, motivated and creative scientific team which is supported both by a flexible and efficient administration and by high-end and innovative technologies.
In November 2013, the Centre for Genomic Regulation (CRG) received the 'HR Excellence in Research' Award from the European Commission. This is a recognition of the Institute's commitment to developing an HR Strategy for Researchers, designed to bring the practices and procedures in line with the principles of the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers (Charter and Code).
The newly established Single Cell and Synthetic Genomics lab at CRG Barcelona is looking for a deep learning expert (at postdoc level or similar), funded for 2 years, to join us in our effort to re-write the code of life.
Two decades after the human genome was first sequenced, our ability to read and interpret the genetic code is still limited. While we have a good understanding of the ‘hardware’ of the genome (that is, the genes), our understanding of the ‘software’ that controls these genes, so called gene regulatory elements, lags behind. Importantly, these elements differ between individuals much more than the actual genes, determining our physical appearance, susceptibility to disease, and even our willingness to engage in extreme sports. A better understanding of this genomic ‘software’ therefore is of high interest to the pharmaceutical and biotechnological industry.
Deep learning is a promising new tool to interpret DNA sequences. However, deep learning is very data hungry, and the human genome is of limited size: The genome may just be too small to truly learn to decipher these highly combinatorial rules. Building on a set of technologies that we have developed (Velten et al., 2017, Schraivogel et al., 2020) our lab therefore takes a different approach: We synthesize 10,000s of new regulatory elements, thereby systematically exploring parts of the very complex space of possibilities. By placing these new pieces of ‘software’ into different ‘environments’ (that is, different types of cells) we collect millions of data points, a rich resource to train deep neural nets. Our synthetic approach allows us to consistently create the type of data most informative to the model. Ultimately, it will not only allow us to interpret the genomic code, but also to write completely new, meaningful pieces of DNA. These can, for example, be used to design viruses that kill cancer cells, but not normal cells.
Your role will be to contribute experience with deep learning to our team of bioinformaticians and experimental biologists.
About the lab
Research in the lab focuses on the development of cutting-edge genomic and bioinformatic technologies to study the regulation of genes during stem cell differentiation. In the lab, we combine ‘synthetic’ single cell genetic screens with deep learning to devise novel, data-guided strategies for the experimental manipulation of gene regulation in hematopoietic and leukemic stem cells.
Whom would we like to hire?
- You are up to date with current developments in the deep learning field
- You have practical experience implementing deep learning on real datasets
- You have publications demonstrating knowledge of deep learning
Education and training
- You hold a Phd (or masters + min. 2 years of relevant industry experience) in computer science, bioinformatics, applied mathematics or a related field
- You are fluent in English
- You have In-depth practical knowledge of deep learning tools (keras, TensorFlow, pytorch, or similar)
- You have strong programming skills
- You have willingness to learn about molecular biology and genetics
- You have willingness to interact with others in an interdisciplinary research team
- You are excellence-driven
The Offer – Working Conditions
- Contract duration: 2 years
- Estimated annual gross salary: Salary is commensurate with qualifications and consistent with our pay scales.
- Target start date: Flexible
We provide a highly stimulating environment with state-of-the-art infrastructures, and unique professional career development opportunities. To check out our training and development portfolio, please visit our website in the training section.
We offer and promote a diverse and inclusive environment and welcomes applicants regardless of age, disability, gender, nationality, ethnicity, religion, sexual orientation or gender identity.
The CRG is committed to reconcile a work and family life of its employees and are offering extended vacation period and the possibility to benefit from flexible working hours.
All applications must include:
- A motivation letter addressed to Dr. Lars Velten
- A complete CV including contact details.
- Contact details of two referees.
All applications must be addressed to Dr. Lars Velten and be submitted online through the "Apply" button below.
- Pre-selection: The pre-selection process will be based on qualifications and expertise reflected on the candidates CVS. It will be merit-based.
- Interview: Preselected candidates will be interviewed by the Hiring Manager of the position and a selection panel if required.
- Offer Letter: Once the successful candidate is identified the Human Resources department will send a Job Offer, specifying the start day, salary, working conditions, among other important details.
Deadline: Please submit your application by 15th February, 2021.
Suggestions: The CRG believes in ongoing improvement and promotes a culture of feedback. This is one of the reasons we have in place, at your disposal as a candidate, a mechanism to gather your suggestions/complaints concerning your candidate experience in our recruitment processes. Your feedback really matters to us in our aim at creating a positive candidate journey. You can make a difference and help us improve by letting us know your suggestions through the following form.
PID2019-108082GA-I00 / AEI / 10.13039/501100011033