The role

Join our dynamic and collaborative research consortium as an Interdisciplinary Scientist, at the exciting crossroads of Computer Science and Developmental Biology. In collaboration with leading labs in Greece and the USA, you will contribute to an exciting HFSP-funded project, aimed at unraveling the intricate molecular and mechanical mechanisms governing ectoderm bilateral symmetry in the crustacean Parhyale hawaïensis embryos. This position offers a unique opportunity to drive innovation at the interface of cutting-edge computational techniques and biological insights.

Key Responsibilities:

Algorithm Development (Python)

• Utilise and enhance existing algorithms to analyse of both morphological and molecular data, acquired through light-sheet fluorescence microscopy and near single cell spatial transcriptomics, respectively.

• Develop innovative computational methods for creating average atlases of Parhyale hawaïensis ectoderm development during embryogenesis.

• Pioneer the creation of algorithms that seamlessly integrate morphological and molecular information within the atlases, pushing the boundaries of interdisciplinary research.

Big Data Analysis

• Implement machine learning techniques to analyse complex datasets and identify patterns, trends, and potential mechanisms underlying symmetry acquisition and maintenance.

Collaborative Research

• Collaborate closely with international partners, including the labs of A. Pavlopoulos in Crete, Greece, and F. Xie in Cleveland, Ohio, USA.

• Attending to the biannual consortium meetings in Marseille, Crete or Cleveland. 

Communication

• Clearly present findings and insights through regular presentations, reports, and publications.

• Engage with both the scientific community and the public to share the project's progress and impact.

Qualifications:

• A Master’s in Computer Science, Computational Biology, Bioinformatics, or a related field.

• Application from Master’s in biology will also be considered

• Programming skills, with knowledge in languages such as Python, R, or similar.

• Experience in data analysis, particularly in the context of biological data.

• Knowledge about machine learning techniques and data integration approaches.

• Excellent communication skills, both written and verbal, for effective collaboration and dissemination of results.

• Ability to work in interdisciplinary teams and adapt to new challenges.

• English fluency (at least B2 on the CEFR).