Computational Proteomics Scientist EMBL-EBI - European Bioinformatics Institute Hinxton, United[...] in Saffron Walden

Our team is responsible for the development and maintenance of world‑leading proteomics resources, including PRIDE, a founding member of the ProteomeXchange Consortium, which captures and disseminates large‑scale proteomics data from the global scientific community. We are also major contributors to international standardization efforts through the Proteomics Standards Initiative (PSI). Single‑cell proteomics is an emerging and rapidly evolving field, generating complex, sparse, and large‑scale datasets. You will play a key role in designing scalable computational methods and standards to support robust analysis, reproducibility, and FAIR data sharing for next‑generation proteomics experiments. As part of a multidisciplinary and highly international team, you will contribute to the development of novel computational methods and software infrastructure for single‑cell proteomics.

Responsibilities

• Design and develop algorithms for single‑cell proteomics data analysis, including peptide/protein quantification, normalization, missing value handling, batch correction, and quality control;
• Develop scalable and high‑performance software components in Python and C++ for processing large‑scale and high‑dimensional proteomics datasets;
• Contribute to machine learning and deep learning approaches for proteomics data analysis, including representation learning, denoising, feature selection, and integrative multi‑omics analysis;
• Participate in the definition and implementation of community data standards, formats, and APIs for single‑cell proteomics under the umbrella of the Proteomics Standards Initiative;
• Work closely with experimental scientists and software engineers to integrate algorithms into production‑ready workflows, databases, and cloud‑based infrastructures;
• Support the transformation of proteomics resources into AI‑ready datasets, enabling downstream use by advanced analytics and large‑scale machine learning systems;
• Collaborate with other EMBL‑EBI teams and international partners to integrate single‑cell proteomics data with resources such as Expression Atlas and UniProt.

Qualifications

• The post holder should have a strong background in computational proteomics, bioinformatics, computer science, or a related quantitative discipline, with typically 3+ years of relevant research or industry experience.
• You will thrive in a collaborative, interdisciplinary environment and communicate effectively with both computational and experimental scientists.
• Strong experience in mass spectrometry‑based proteomics, ideally including single‑cell or low‑input proteomics;
• Proven experience developing algorithms for proteomics data analysis;
• Excellent programming skills in Python and C++, with a focus on performance, maintainability, and reproducibility;
• Experience with machine learning and/or deep learning frameworks (e.g. PyTorch, TensorFlow, JAX, scikit‑learn);
• Solid understanding of statistical methods for high‑dimensional and sparse biological data;
• Experience working with large‑scale datasets, including efficient I/O and memory‑aware data processing;
• Proficiency with version control systems such as Git;
• Ability to work independently, manage multiple priorities, and communicate results clearly in an international environment;
• Experience with proteomics data formats and standards (e.g. mzML, mzIdentML, mzTab, Parquet‑based formats);
• Familiarity with high‑performance computing (HPC), GPU acceleration, or parallel computing;
• Experience with workflow systems (e.g. Nextflow) and containerised environments (Docker, Singularity);
• Knowledge of cloud‑based infrastructures and scalable data processing frameworks;
• Interest in FAIR data principles, open science, and community‑driven standardisation;
• Experience integrating proteomics data with other omics modalities (e.g. transcriptomics, metabolomics).