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Centre for Genomic Regulation (CRG)

Enhancing Spectrum Clarity: A Neural Network Approach for Signal-Noise Discrimination in DIA MS2 Data Centre for Genomic Regulation (CRG) in Spain

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available

Deadline

Expired

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Country

Spain

University

Centre for Genomic Regulation (CRG)

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Keywords

Computer Science
Deep Learning
Biology
Mass Spectrometry
Artificial Neural Network
Bioinformatic
Machine learning

About this position

This fully funded PhD position at the Centre for Genomic Regulation (CRG) offers an exciting opportunity to advance computational proteomics by developing neural network models for signal-noise discrimination in DIA MS2 spectra. The project addresses a key challenge in mass spectrometry workflows—distinguishing true peptide signals from noise, which currently limits sensitivity and accuracy in large-scale proteomics experiments.

The successful candidate will design and implement deep neural network architectures to classify MS2 peaks as signal or noise, leveraging variations in peptide intensity during elution. The resulting models will be integrated into spectrum cleaning workflows, enhancing peptide identification accuracy and enabling tailored detection of measurement artifacts. The research will deliver a robust noise classification model, customizable for lab-specific data, and develop new algorithms for peptide identification, supporting future applications across diverse experimental setups.

Doctoral training is enriched by international secondments: a one-month placement at KTH (Käll) in year 1 to integrate pretrained models for DIA-based MS2 peak classification; a one-month secondment at Sanger (Lehner) in year 2 for model interpretability training; and a one-month placement at BIOCEV (Novák) in year 3 to apply developed models to HDX/FPOP data, broadening expertise in structural proteomics.

The ideal applicant will have a background in bioinformatics, computer science, or a related field, with strong programming skills and experience in machine learning or deep learning. Data analysis skills are highly valued, and familiarity with proteomics is advantageous. The position is open to applicants of any nationality, subject to the MSCA mobility rule (not having resided or carried out main activity in the host country for more than 12 months in the 3 years before recruitment).

Funding is provided through the Marie Skłodowska-Curie Doctoral Network (MSCA-DN), covering full salary, mobility allowance, and family allowance (where eligible) for 36 months. The research will be conducted within the ProtAIomics European Innovative Doctoral Network, offering a collaborative and interdisciplinary environment.

To apply, visit the ProtAIomics website and submit your application online before March 23, 2026. For more information, see the full project details on FindAPhD.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

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