Do you want to train in Forest Genetic Resources and Omic data
analysis,including Machine Learning?

We offer a CSIC JAE Intro SCHOLARSHIP for graduate students and recent
graduates at the Institute of Forest Sciences (ICIFOR-INIA-CSIC), Madrid
(Spain).

💸 8 months / €700 per month / 20 hours per week

APPLICATION DEADLINE: April 16, 2025

Omics Data Analysis for the Characterization of Mediterranean Forest
Species, Including Machine Learning. PI: Dr. Irene Cobo Simón

Climate change and biotic and abiotic threats are severely affecting
Mediterranean forest ecosystems, where key species such as pines play a
fundamental role in ecological stability. Genomics and systems biology
have opened new avenues for understanding the resilience of these species
to stress factors. However, the integration of multi-omics data remains
a challenge in predicting adaptive responses. This scholarship aims
to provide the student with experience in the generation and analysis
of omics data in Mediterranean forest species, combining experimental
laboratory work with bioinformatics analysis and computational modeling.

Objectives 
- Familiarize the student with the main laboratory techniques for
  generating omics data.
- Train them in bioinformatics tools for the analysis and/or integration
  of genomic, transcriptomic, and/or epigenomic (miRNAs) data.
- Develop skills in applied statistics and machine learning for
  predicting adaptive traits in forest species.
- Foster critical thinking in the interpretation of omics data and its
  application in forest management and conservation.
- Transfer of results to the public-private sector (MITECO and TRAGSA).

The training plan is structured in two main blocks:
1. Generation and processing of data in the laboratory (40%): DNA and/or
   RNA extraction, library preparation for next-generation sequencing
   (NGS), Quality control and data preprocessing.

2. Data analysis and integration (60%): Processing omics data using
   bioinformatics tools, Multivariate statistical analysis and
   dimensionality reduction techniques, Application of models (e.g.,
   Bayesian, machine learning) to predict functional traits, Data
   visualization and interpretation of results in the context of forest
   adaptation.

This training program will equip the student with key competencies in
molecular biology, bioinformatics, and omics data analysis, highly
sought after in biotechnology, genetic resource conservation, and
forest improvement. Additionally, experience in machine learning and
predictive modeling will provide tools applicable in other emerging
fields of computational biology and systems ecology. The combination
of experimental and computational training will ensure a versatile and
competitive profile in both the academic and professional spheres.

Please, if you are interested, kindly send an email briefly
explaining your background and motivation for the scholarship to:
irene.cobo@inia.csic.es

More information: tinyurl.com/yv87j7cp
https://sede.csic.gob.es/tramites/programa-jae/convocatoria-jae-intro-icu-2025

Irene Cobo <irenecobo88@gmail.com>

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