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Job Description
We are seeking a highly accomplished and interdisciplinary Senior Research Fellow to lead research efforts at the interface of artificial intelligence and microbial genomics. The ideal candidate will have deep expertise in machine learning and bioinformatics, with a strong track record of applying AI to next-generation sequencing data-particularly Nanopore sequencing-to uncover novel biological insights. This role involves pioneering work in microbial diversity, phage genomics, epigenetics, and potentially synthetic biology, including the study of nucleobase modifications and non-canonical bases.
Key Responsibilities:
- Lead the development of AI/ML models for microbial genome analysis, including metagenomic, classification, and prediction tasks
- Collaborate with experimental biologists and computational scientists to integrate multi-omics data and drive discovery in microbial and synthetic biology.
- Apply signal processing and deep learning techniques to raw Nanopore sequencing data for the detection of base modifications and non-canonical bases
- Mentor junior researchers and contribute to the training of graduate students and postdoctoral fellows.
- Publish impactful research and contribute to competitive grant proposals and strategic initiatives.
- Maintain and optimize scalable, reproducible pipelines for genomic data analysis.
Qualifications
- PhD in Computational Biology, Bioinformatics, Computer Science, or a related field.
- Minimum 5 years of postdoctoral or equivalent research experience in AI/ML applied to genomics or biological signal analysis.
- Strong programming skills in Python or R, and experience with machine learning frameworks (e.g., PyTorch, scikit-learn).
- Familiarity with microbial genomics, metagenomics, and genome annotation tools.
- Excellent problem-solving skills, communication abilities, and a collaborative mindset.
Preferred Skills:
- Knowledge of deep learning techniques applied to biological sequences or structures.
- Experience with large-scale data integration and visualization.
- Strong publication record and experience in interdisciplinary collaboration.
