Postdoc Neuroscience Computational Biology

Johnson & Johnson Johnson & Johnson · Pharma · Spring House, PA +1

Postdoctoral Scientist role in neuroscience computational biology at Johnson & Johnson, focusing on analyzing large-scale omics data to understand neuropsychiatric and neurodegenerative disorders using AI-enabled, data-driven drug discovery approaches. Responsibilities include novel target identification, genetics association analysis, proteome-wide multi-task learning, and patient stratification. The role involves developing computational pipelines, analyzing multi-omics data, and publishing findings.

What you'd actually do

  1. Novel target identification using integrative approaches in neuropsychiatric disorders.
  2. Large-scale genetics association analysis.
  3. Proteome-wide multi-task learning for target and biomarker discovery in brain diseases
  4. Precision patient stratification and characterization of subtype-specific genomic signatures
  5. Analyze large-scale genetics, proteomics, and other omics modalities using advanced statistical and computational methods.

Skills

Required

  • PhD in a quantitative field (e.g., Computational Biology, Systems Biology, Biostatistics, Bioinformatics, Statistical Genetics, Computer Science, or related discipline).
  • Hands-on experience applying machine learning methods (e.g., multi-task learning; supervised and unsupervised learning) to biological problems.
  • Proficiency in R and/or Python; working knowledge of Bash and SQL; and familiarity with high-performance computing environments.
  • Experience integrating multi-omics data (e.g., network-based approaches).
  • Excellent written and verbal communication skills, with the ability to communicate technical results to diverse stakeholders.
  • Ability to work independently as well as collaboratively in a multidisciplinary team environment.

Nice to have

  • Experience or domain knowledge in neuropsychiatric and/or neurodegenerative diseases.
  • Experience with bulk or single-cell transcriptomics and/or high-throughput proteomics.

What the JD emphasized

  • Strong publication record in relevant areas.

Other signals

  • AI-enabled drug discovery
  • machine learning approaches
  • computational biology
  • multi-task learning
  • integrative approaches