Postdoctoral research position in selective autophagy research using bioinformatics approaches

Responsibilities:

The successful candidate will have a lead role in a research team working towards developing a comprehensive and user-friendly data portal offering integrated access to high-quality, experimentally derived information regarding LIR-motif::LC3 interactions as well as bioinformatics tools for characterizing and cataloguing novel selective autophagy receptor/adaptor/scaffold proteins.

They will be expected to conduct high-quality research and be very open to collaboration with other members of the BRL and project partners. In particular, they will (a) lead the design and implementation of novel computational resources for selective autophagy research, (b) support project management (including the preparation and submission of project reports), (c) supervise junior researchers, (d) contribute to dissemination activities (including drafting manuscripts, participating in meetings and conferences abroad), and (e) participate in preparation of relevant grant proposals.

Qualifications:

Required qualifications The successful candidate is expected to have: • PhD in Bioinformatics/Computational Biology or in related disciplines with research experience in Bioinformatics/Computational biology. • Promising academic record & high motivation in developing a long-term career in research. • Excellent understanding of basic concepts in molecular cell biology, molecular evolution, and protein structure & interactions. Experience in eukaryotic systems is a plus. • Excellent time management, communication, and organizational skills. • Proficiency in English, including experience in scientific writing. • First author publications in peer-reviewed scientific journals.

Technical skills • Proficiency in programming in at least one high-level programming language (e.g. C/C++, Perl, Python). • Extensive experience with relevant data types and resources (including protein sequences and structures, genomic/proteomic data resources etc). • Extensive experience in applying statistical methods, including Machine/Deep learning methods, to develop/validate/test prediction models in Bioinformatics. • Web and database programming experience (e.g. REACT, Flask, FASTapi, REST, PostgreSQL/MySQL) is desirable but not essential. • Being familiar with FAIR data principles and other technologies such as Biomedical Text Mining, containerisation, High Performance Computing, is a plus.