DoctoratClosing soon

PhD Student in Artificial Intelligence - « Interpretable Vector Symbolic Architectures for scalable Genome Interpretation »

EURAXESS hosting organisation · Plusieurs pays européens

- work environment: The recruited person will join the « AI for Genome Interpretation » directed by Daniele Raimondi at l’IGMM. The project will be developed in an interdisciplinary and international environment . The core focus of D. Raimondi’s “AI for Genome Interpretation” lab at the Institut de Génétique Moléculaire de Montpellier (IGMM) is the development of tailor-made Machine Learning (ML) and Neural Networks (NNs) models for Genome Interpretation (GI)1,2. GI is the umbrella term describing the bioinformatics approaches devoted to understanding the relationship between genotype and phenotype, targeting open biological problems ranging from human clinical genetics to plant biology. My aim is to develop a new paradigm of computational methods to overcome the limitations of current GI

At a glance

Where: Multiple European countries
Deadline: 2026-09-25
Funding: EUR 2300 (parsed from page — verify)
Board: EURAXESS

Who can apply

Criteria stated by the funder on the official source.

Nationality

EURAXESS vacancies rarely print a nationality table. Confirm whether applicants from Tunisia can apply and whether a residence/work permit pathway exists on the employer page.

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Education

Research Field Computer science Education Level Master Degree or equivalent Skills/Qualifications Candidates should have a Master's degree, or equivalent, in Computer Science, Artificial Intelligence, Machine Learning, Bioinformatics, Computational Biology, Applied Mathematics, or a related discipline. Strong candidates should have: - solid Python programming skills and pytorch; - bioinformatics or computational genomics knowledge; - good knowledge of machine learning and statistica

Non indiqué

Funding and what it does not cover

Funding details were taken from the official programme page text. Confirm the current call before applying.