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Early Stage Researcher: Machine Learning predictions of peptide behaviour à Ghent

Description du poste

Marie-Sklodowska Curie PROTrEIN-ITN Early Stage Researcher student) position:

Machine Learning predictions of peptide behaviour for improved identification of modified peptides 

About VIB 

The Flemish Institute for Biotechnology (VIB) in Ghent is a life sciences research institute that operates in close partnership with Ghent University. Dedicated to cutting-edge basic research with a strong focus on translating scientific results into pharmaceutical, medical, agricultural, and industrial applications. 

Thanks to the hard work of many people for almost 25 years and the support of sustained investment by the government of Flanders, VIB is widely recognized as an established and world-leading knowledge center in life sciences and biotechnology with an excellent reputation in technology transfer. 

The framework agreement between VIB and Ghent University stipulates the ability of VIB researchers to register in the Doctoral Programme at Ghent University and obtain their at Ghent University. Today Ghent University attracts over 43,000 students, with a foreign student population of about 10% (42% of students) and employs around 7,300 academic staff members. Ghent University is 66th in the Shanghai ranking and 103rd in the Times ranking. 

About the Project

PROTrEIN () is a European Innovative Training Network composed of 11 beneficiaries, and 6 partner organizations, from the academic and non-academic sectors (including two SMEs and two large companies).

The network’s mission is to train a new generation of computational proteomics researchers by providing them an inter-sectoral and interdisciplinary set of skills to tackle the main challenges in the field and improve their future employability.

This position is for the Machine Learning research part of the project. Please find more information at The overall objective of this research project is to enable a much more sensitive yet reliable identification of (modified) peptides through DDA and DIA approaches. This will be achieved through four sub-goals: first, a novel predictor will be made for ion mobility behaviour (collisional cross-section) of (modified) peptides; second, a novel predictor will be made for the retention time of (modified) peptides; third, based on these two predictors alongside our existing MS2PIP predictor for fragmentation spectra, advanced theoretical spectral libraries will be built for DIA-based identification; fourth, we will add these two predictors to complement the core modules in our existing cloud-based ionbot tool (


  • Prof. Dr.
  • Prof. Dr.

  • Methodology

    Machine Learning approaches to predict analyte behaviour; gradient boosting and deep learning algorithms will be employed, based on large amounts of available public data.


    Required skills

  • Python programming (Numpy, Pandas)
  • Experience with a Python Machine Learning library Scikit-learn).

  • Nice to have

  • Experience in Deep Learning
  • Programming in C, C++

  • Planned Secondments

  • Host: FHOOE (V. Dorfer), Duration: 1 Month; When: Year 1; Goal: Analysis of chimeric MS2 spectra.
  • Host: CRG (E. Sabido), Duration: 3 Month; When: Year 2; Goal: Experience in DIA proteomics acquisition methods.
  • Host: EMBO (B. Pulverer), Duration: 2 Weeks, When: Year 3, Goal: Scientific writing and editing.

  • Enrolment in doctoral programs

    in Bioinformatics from Ghent University

    Informations supplémentaires

    Formation requise
    Heures de travail par semaine
    8 - 40
    Type de Contrat
    Informatique / Télécommunication
    Permis de conduire BE/EU exigé
    Voiture exigée
    Lettre de motivation exigée

    Informatique / Télécommunication | Alternance/Apprentissage | Autres

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