IMU Biosciences
IMU Biosciences
IMU Biosciences este o companie de biotehnologie cu sediul în Londra, care dezvoltă tehnologii de imunologie cu dimensionalitate ridicată pentru cercetare clinică, diagnostic și dezvoltarea medicamentelor. Platforma sa multi-omică și de mare capacitate analizează peste 100 de milioane de puncte de date imune dintr-o probă de sânge de 2 ml și diferențiază în timp real aproximativ 2.500 de subpopulații de celule imune. Setul de date imune rezultat, realizat la scară populațională, sprijină aplicații precum screeningul predictiv pentru transplantul de celule stem hematopoietice, evaluarea respingerii grefei în transplantul de organe solide și identificarea semnăturilor imune asociate cu răspunsul la tratament și cu evenimentele adverse în imuno-oncologie. IMU Biosciences colaborează cu grupuri de cercetare clinică, companii farmaceutice și de biotehnologie și parteneri din domeniul diagnosticului pentru a transforma fenotiparea imună în instrumente clinice și comerciale practice.

Bioinformatics Software Engineer – Flow Cytometry

Build and maintain regulated machine learning pipelines for flow cytometry at IMU Biosciences. The role covers annotation, model tracking, reproducibility, and compliance for a precision medicine platform.

Descriere

  • Run automated cell annotation workflows on large cytometry datasets.
  • Produce cell membership files, cell subset ratio tables, and automated reports that flag outliers for quality control.
  • Apply locked automated gating ensembles to cross-validation and evaluation data partitions.
  • Track and document gating exposure metrics for each patient.
  • Set and lock baseline models and numerical stability criteria for flow cytometry feature extraction pipelines.
  • Maintain the formal model lifecycle registry, including versioning, freezing, and locking gating ensembles and candidate predictive models.
  • Refit shortlisted machine learning models on larger cohorts and freeze final models before blinded clinical evaluations.
  • Commit versioned code, pin Conda and Docker environment specifications, and log random seeds and model hyperparameters.
  • Act as the primary code reviewer across the computational pipeline.
  • Apply controls against data leakage, verify data integrity, and uphold modular coding standards.
  • Take part in Independent Method Reviews, traceability audits, and regulatory compliance checks.
  • Work with Machine Learning Engineers, Biostatisticians, and Clinical Scientists.
  • Report to the Lead Data Scientist.

Cerințe

  • A master’s degree or Ph.D. in Bioinformatics, Statistics, Computational Biology, Computer Science, Bioengineering, or a related quantitative discipline.
  • At least three years of professional experience with high-throughput flow cytometry data in a regulated, academic, or biotech setting.
  • Fluency in Python and experience with NumPy, Pandas, SciPy, scikit-learn, and PyTorch.
  • Demonstrated use of software engineering practices, including Git, CI/CD, workflow managers such as Nextflow, Docker environment pinning, and ML artifact tracking.
  • Familiarity with flow cytometry feature generation and batch-effect harmonisation.
  • Familiarity with cross-validation leakage risks in high-dimensional flow cytometry predictive modelling.
  • Existing right to work in the UK.

Beneficii

  • Competitive salary and company bonus scheme.
  • Pension.
  • Health insurance.
  • Life assurance.
  • Income protection.
  • A fast-paced, collaborative startup environment.
  • Opportunities for career development and progression.

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