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Machine Learning Expert for Nanopore Sequencing Analysis
hace 3 semanas
Adapting nanopore sequencing basecallers for nucleotide modification detection using incremental learning and anomaly detection techniques is a fascinating task that requires strong foundation in signal processing and experience in computational biology and nanopore sequencing data.
- The role focuses on developing and optimizing machine learning models for the task.
- Applying signal processing techniques to improve nucleotide modification detection is crucial for accurate results.
An innovative approach combining hybrid and modular nucleic acids is being pursued in this project, requiring close collaboration with wet-lab researchers to validate computational predictions.
We are seeking a skilled Predoctoral Researcher to join our team and contribute to this exciting project.
Key Responsibilities:
- Develop and optimize machine learning models for adapting nanopore sequencing basecallers.
- Apply signal processing techniques to improve nucleotide modification detection.
- Collaborate with wet-lab researchers to validate computational predictions.
Requirements:
- Strong foundation in signal processing.
- Experience in computational biology and nanopore sequencing data (preferred).
Qualifications:
- M.Sc. in Computer Science, Biomedical Engineering, Bioinformatics, Biology, or a related field.
- Familiarity with the implementation and application of machine learning methods and neural networks deep learning frameworks.
- Proficiency in Python, R, and familiarity with scientific computing libraries.
- Strong problem-solving skills and ability to work in a collaborative research environment.
- Advanced English communication skills.
Benefits:
- A fully funded PhD position with a competitive salary.
- Opportunity to work on cutting-edge research in nanopore sequencing and machine learning-oriented bioinformatics.
- A collaborative and interdisciplinary work environment engaging with wet lab research.
- Support for career development and encouragement of active participation in academic events relevant to early-stage researchers.