Postdoc in multi-omics machine learning and computational cancer biology

Postdoc in Multi-Omics Machine Learning and Computational Cancer Biology

The lab is part of the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) and is based at SciLifeLab Stockholm and KTH. We develop and apply computational approaches for cancer precision medicine, with a particular focus on understanding the molecular mechanisms underlying cancer aggressiveness, metastasis and treatment resistance. The research combines cancer biology, multi-omics and computational methods, with a strong track record of interdisciplinary research and publications in leading journals. More information about the lab is available at: A Lundberg Lab.

We will integrate multiple data modalities (biological, clinical/pathological data and medical imaging), with an emphasis on developing interpretable and biologically meaningful models. Methods include machine/deep learning, image analysis, computational pathology, multi-omics integration and foundation models.

The position is suitable for a researcher with a strong computational background and demonstrated expertise in machine learning, deep learning or related areas, combined with an interest in cancer biology and biomedical applications.

The position also includes opportunities to contribute to grant applications, research networks, education and mentorship.

Qualifications

Requirements

• A doctoral degree in computer science, machine learning, computational biology, bioinformatics, or a related field. This eligibility requirement must be fulfilled by the time the appointment decision is made.
• A strong research profile in machine learning, deep learning, computational biology, or related computational fields, with a demonstrated interest in applying computational methods to cancer or other biomedical research.
• Programming skills in Python and/or R, and experience developing, implementing, and evaluating computational or machine learning methods.
• Solid knowledge of statistics, biostatistics, and multivariate data analysis, including appropriate methods for model evaluation and validation.
• Excellent written and spoken English, as English is used in day-to-day work.
• Experience in:
  • Working with large-scale, high-dimensional biological or biomedical datasets and integrating heterogeneous data types.
  • One or more of the following areas: multi-omics data analysis and integration, multimodal machine learning, medical imaging, computational pathology, image analysis, biological data integration, or foundation models.
  • Unix/Linux environments and high-performance computing systems.
  • Scientific writing, scientific publications, and/or grant applications.

    Preferred qualifications

    • A doctoral degree obtained within the three years preceding the application deadline.
    • Experience in:
      • Developing interpretable or explainable machine learning models.
      • Applying machine learning to cancer biology, cancer genomics, or precision medicine.
      • Working with large-scale public or clinical datasets and independently developing research projects.
      • Teaching, mentoring, or supervision.
      • Well-developed analytical and problem-solving skills.
      • Ability to work effectively in an interdisciplinary and collaborative research environment.
      • A structured and organized working style.
      • Awareness of diversity and equal opportunities, with a particular focus on gender equality.
      • Great emphasis will be placed on personal skills.