Created in 2005 by the Generalitat de Catalunya (Government of Catalonia) and the University of Barcelona, IRB Barcelona is a Severo Ochoa Centre of Excellence—a seal that was awarded in 2011.

The institute is devoted to conducting research of excellence in biomedicine and to transferring results to clinical practice, thus improving people’s quality of life, while simultaneously promoting the training of outstanding researchers, technology transfer, and public communication of science. Its 28 laboratories and seven core facilities address basic questions in biology and are orientated to diseases such as cancer, metastasis, Alzheimer’s, diabetes, and rare conditions.

IRB Barcelona is an international centre that hosts 400 members and 30 nationalities. It is located in the Barcelona Science Park. IRB Barcelona forms part of the Barcelona Institute of Science and Technology (BIST) and the “Xarxa de Centres de Recerca de Catalunya” (CERCA). 

IRB Barcelona is seeking a talented and highly motivated Postdoctoral Researcher to join the Structural Bioinformatics and Network Biology group (https://sbnb.irbbarcelona.org), led by Dr. Patrick Aloy, to work on Computational Systems Medicine.

Large-scale small molecule bioactivity data are not routinely integrated in daily biological research to the extent of other ‘omics’ information. Compound data are scattered and diverse, making them inaccessible to most researchers and not suited to standard statistical analyses. The urge to couple chemical and biological data to cutting-edge machine learning has prompted us to develop new strategies for data integration and knowledge representation, especially in the form of heterogeneous networks and vector-like descriptors. In particular, we generated the Chemical Checker, which is currently the largest repository of small molecule bioactivity signatures (Duran-Frigola et al. 2020 Nat Biotechnol; Bertoni et al. 2021 Nat Commun). To complement it, we created the Bioteque, a repository of context-dependent biological signatures based on a gigantic knowledge graph representing most currently known biology (Fernández-Torras et al. 2022 Nature Commun). This common vector format to represent biology and chemistry helps blending the two worlds. We are now developing a generalized connectivity mapping, as a form of virtual phenotypic screening, to discover novel chemical or genetic modulators able to revert the specific signatures of disease and ‘cancel out’ the phenotypic traits of complex disorders. For instance, we have discovered compounds able to revert AD signatures in vitro and in vivo, neutralizing the cognition deficiencies in AD mouse models (Pauls et al. 2021 Genome Med). We now aim at finding compounds to globally modulate the activity of a specific set of targets, selected from the SARS-CoV-2 – Human contactome (Kim et al. 2022 Nat Biotecnol) derived in the frame of the European RiPCoN project (ref: 101003633), as well as other complex diseases. All in all, the incorporation of high-content biological and chemical descriptors to the drug discovery process will trigger the identification of novel compounds, finally enabling systems precision medicine.


DUTIES: 

The successful candidate shall be responsible for the implementation of ML-based Generative Models (i.e. cVAEs or GANs) to create new small molecules that fulfill the required polypharmacological properties to modulate the SARS-CoV-2 – Human contactome, and other complex diseases.
 

EXPERIENCE, KNOWLEDGE, SKILLS:

Must Have – Required: 

• Education: Bachelor in Biosciences, Chemistry, Pharmacy or Engineering degree in Computer Sciences. PhD in Bioinformatics, machine learning or related areas.
  
• Experience: previous experience on the use of machine learning and data science techniques, as well as in the development of methods to process and integrate omics experiments. Strong publications record according to his/her career stage.
  
• Skills:
  
  • Excellent programming and scripting skills, with deep knowledge of Python.
    
  • Deep knowledge of statistical modelling methods and the normalization and integration of omics data.
    
  • Advanced knowledge of machine learning techniques (TensorFlow/AdaNet).
    
  • Competent in the use of HPC queue systems, virtual machines (OpenNebula) and Grid Containers (Docker, Singularity).
    
  • Excellent interpersonal and communication skills. Highly motivated. Fluency in English.

Desirable:

•   Experience: Previous experience working with biological data and in an international environment.
• Skills: Knowledge of ML-based generative models (e.g. cVAEs, GANs, etc)
  

WORKING CONDITIONS & ENTITLEMENTS:

• Working conditions: Employed in compliance with Spanish legislation and regulations under a full-time contract. Employees receive the benefits of the Spanish Social Security system covering sickness, maternity/paternity leaves and injuries at work.
  
• Training and Career: Postdoctoral researchers joining IRB Barcelona gain access to the Institute’s advanced research training and career development opportunities, all within in a competitive international environment. Courses and workshops on themes of particular interest to postdocs are offered regularly by the Institute.
  
• International environment: Nearly 90 Postdoctoral researchers (more than a half non-Spanish nationals) are currently working at IRB Barcelona.

HOW TO APPLY & SELECTION PROCESS:  

• Deadline for applications: 31/03/2023 (If no suitable candidate is found, the deadline will be extended) 
• Number of positions available: 1
• Selection process:
  • Pre-selection: Will be based on CV, motivation letter, experience, management of research and innovation. 
  • Interviews: Short-listed candidates will be interviewed.
  • Job offer: Will be sent to the successful candidate after the interview. 

Note: The strengths and weaknesses of the applications will be provided upon request.