Technical reseracher - Distributed machine learning for wearable sensors data in stroke (RE1)

Job Reference

92_21_LS_CB_RE1

Position

Technical reseracher - Distributed machine learning for wearable sensors data in stroke (RE1)

Closing Date

Friday, 16 April, 2021
Reference: 92_21_LS_CB_RE1
Job title: Technical reseracher - Distributed machine learning for wearable sensors data in stroke (RE1)

About BSC

The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful supercomputers in Europe, and is a hosting member of the PRACE European distributed supercomputing infrastructure. The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress. BSC combines HPC service provision and R&D into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over 700 staff from 49 countries.

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Context And Mission

The Computational Biology group, led by ICREA professor Alfonso Valencia, is looking for a postdoctoral researcher to work in the context of distributed solutions for machine learning in cerebrovascular conditions (stroke) using sensors data from wearable devices.

The Life Sciences Department at the BSC integrates the independent research of senior scientists that work on various aspects of computational biology, ranging from bioinformatics for genomics to computational biochemistry and text mining. The Computational Biology group (http://life.bsc.es/compbio) is involved in multiple projects covering a wide range of topics including multilayer network modeling, epigenomics and computations systems biology.

The candidate will work in collaboration with senior researchers in the Computational Biology Group of the Life Sciences Department as well as other research groups at the BSC. The work is in the framework of the research lines of the group, which include applications of machine learning in personalized medicine, cell-level simulations including metabolic models, sequence-based protein coevolution and genomics.

The Researcher will work in a highly sophisticated HPC environment, will have access to state-of-the-art systems and computational infrastructures, and will establish collaborations with experts in different areas both at international and local levels. In particular, the Researcher’s tasks will involve applying machine learning techniques in the context of the European project AI-SPRINT (https://cordis.europa.eu/project/id/101016577). The task will focus on the collection, analysis and modeling of sensors data from wearable devices and the use of the distributed machine learning library dislib (https://www.bsc.es/research-and-development/software-and-apps/software-list/dislib).

Key Duties

  • Application of machine learning methods, with special emphasis on distributed solutions, for the analysis and interpretation of sensors data.
  • Use predictive computational methods based on statistical and inference approaches to model complex biological processes
  • Integration of sensors data sources using advanced data mining and machine learning techniques.
  • HPC solutions for machine learning applications in life sciences.
  • Preparation and presentation of scientific articles.
  • Establish and maintain collaborations with national and international researchers.

Requirements

  • Education
    • PhD in computer science or bioinformatics with a machine learning component.
    • Alternatively, an MSc on machine learning or Bioinformatics, with a strong computer science background, or background on applied mathematics/physics with demonstrated experience in machine learning methods.
  • Essential Knowledge and Professional Experience
    • Experience in machine learning methodologies.
    • Interest in digital technologies for life sciences.
  • Additional Knowledge and Professional Experience
    • Knowledge and experience in biomedical research.
    • Knowledge and experience in data science methodologies:
      ▪ Data pre/post-processing (feature selection, dimensionality reduction, plotting and visualization).
      ▪ Time-series analysis.
      ▪ Deep learning theory and frameworks (PyTorch, Keras, TensorFlow).
      ▪ High-performance computing (HPC).
      ▪ Fundamentals of linear algebra.
      ▪ Bayesian inference.
    • Programming: Python (scikit-learn, numpy, matplotlib), Matlab, R, Java, C, C++, Git.
  • Competences
    • Fluency in spoken and written English.
    • Capacity to explore new research lines .
    • Good communication and presentation skills.
    • Ability to work both independently and within a team.

Conditions

  • The position will be located at BSC within the Life Sciences Department
  • We offer a full-time contract, a good working environment, a highly stimulating environment with state-of-the-art infrastructure, flexible working hours, extensive training plan, tickets restaurant, private health insurance, fully support to the relocation procedures
  • Duration: Temporary - 1 year, renewable renewable
  • Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona
  • Starting date: asap

Applications Procedure

All applications must include:

  • A Cover Letter with a statement of interest in English, including two contacts for further references - Applications without this document will not be considered

  • A full CV in English including contact details


Deadline

The vacancy will remain open until suitable candidate has been hired. Applications will be regularly reviewed and potential candidates will be contacted.

Diversity and Equal Opportunity Employment

BSC-CNS is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law.

Application Form

Please, upload your CV document using the following name structure: Name_Surname_CV
Files must be less than 3 MB.
Allowed file types: txt rtf pdf doc docx.
Please, upload your CV document using the following name structure: Name_Surname_CoverLetter
Files must be less than 3 MB.
Allowed file types: txt rtf pdf doc docx zip.
Please, upload your CV document using the following name structure: Name_Surname_OtherDocument
Files must be less than 10 MB.
Allowed file types: txt rtf pdf doc docx rar tar zip.
** Consider that the information provided in relation to gender and nationality will be used solely for statistical purposes.