Predoctoral researcher in Machine Learning for Nanopore Sequencing

Center for Biological Research "Margarita Salas"-CSIC; Granada; Presencial; EUR 25.000 - 35.000; Jornada completa

Descripción de la vacante

A predoctoral position is available in a leading research institute in Spain, focusing on adapting nanopore sequencing basecallers for nucleotide modification detection. This role offers a chance to pursue a PhD in chemical biology and machine learning, with competitive funding and a collaborative environment.

Servicios

Fully funded PhD position

Competitive salary

Career development support

Participation in academic events

Formación

• Experience in computational biology and nanopore sequencing data preferred.
• Familiarity with machine learning methods and deep learning frameworks.

Responsabilidades

• Assist in developing machine learning models for nanopore sequencing.
• Apply signal processing techniques for nucleotide modification detection.
• Work closely with wet-lab researchers to validate predictions.

Conocimientos

Signal Processing

Problem Solving

Collaboration

Advanced English

Educación

M.Sc. in Computer Science

M.Sc. in Biomedical Engineering

M.Sc. in Bioinformatics

M.Sc. in Biology

Herramientas

Python

R

Descripción del empleo

A predoctoral position is available in Dr. Ruth Pérez’s group at the Centro de Investigaciones Fernández. The CIB Margarita Salas, part of the Spanish National Research Council (CSIC), is a leading multidisciplinary research institute in Madrid, Spain. Internationally recognized for its excellence in molecular biology, biomedicine, and biotechnology, it fosters cutting-edge science and innovation at the interface of biology and health.

We are seeking a Predoctoral Researcher to join the ERC Chem2Sense project

The role focuses on adapting nanopore sequencing basecallers for nucleotide modification detection using incremental learning and anomaly detection techniques. This is an excellent opportunity for a candidate to pursue a PhD in chemical biology and machine learning for nanopore data analysis.

Key Responsibilities :

1. Assist in developing and optimizing machine learning models for nanopore sequencing basecallers.
2. Apply signal processing techniques to improve nucleotide modification detection.
3. Contribute to an innovative approach combining hybrid and modular nucleic acids.
4. Work closely with wet-lab researchers to validate computational predictions.

Requirements :

1. Strong foundation in signal processing.
2. Experience in computational biology and nanopore sequencing data (preferred).

Qualifications :

1. M.Sc. in Computer Science, Biomedical Engineering, Bioinformatics, Biology, or a related field.
2. Familiarity with machine learning methods, neural networks, and deep learning frameworks.
3. Proficiency in Python, R, and scientific computing libraries.
4. Strong problem-solving skills and ability to work collaboratively.
5. Advanced English communication skills.

What We Offer :

1. Fully funded PhD position with a competitive salary.
2. Opportunity to work on cutting-edge research in nanopore sequencing and machine learning bioinformatics.
3. A collaborative and interdisciplinary environment engaging with wet lab research.
4. Support for career development and participation in academic events for early-stage researchers.

How to Apply :

Please submit your CV and a brief cover letter. Applications will be reviewed on a rolling basis.

Reference: J-18808-Ljbffr