PhD Fellow (2 positions), AI for Malaria Surveillance (AIMaR/Google-funded)

The African Centre of Excellence in Bioinformatics and Data-Intensive Sciences (ACE) at the Infectious Diseases Institute, Makerere University, invites applications for two (2) PhD fellowship positions under the AIMaR project, funded by Google. About the project AIMaR is a multidisciplinary research project that applies advanced artificial intelligence tools, including the EVE (Evolutionary model of Variant Effect) framework and AlphaFold 3, to predict and characterise mutations across the Plasmodium falciparum proteome. The project aims to identify mutations relevant to malaria surveillance, drug resistance and vaccine design, and to associate these mutations with epidemiological and clinical parameters using a decade's worth of annotated samples collected from multiple sites across Uganda. About the role The PhD fellows will be embedded within the AIMaR research team and will develop and lead independent doctoral research in line with the project's aims. Fellows are expected to produce at least two first-authored peer-reviewed publications during their doctoral training, benefiting from mentorship by experienced researchers across bioinformatics, structural biology, epidemiology and malaria science. Key responsibilities - Develop an independent doctoral research proposal in malaria bioinformatics or computational biology, aligned with the aims and scope of the AIMaR project, in consultation with the project's principal investigators. - Design and execute the doctoral project from conceptualisation through to data analysis, interpretation and dissemination. - Prepare and defend a PhD thesis meeting all requirements of Graduate Studies. - Write a minimum of two first-authored peer-reviewed publications arising from the doctoral research. - Attend and present research findings at AIMaR team meetings, institutional seminars, and national and international scientific conferences. - Where relevant, provide research mentorship and guidance to MSc students engaged in the AIMaR project. - Contribute to project reports and deliverables as required by the project team and the funder. - Maintain thorough documentation of research activities, datasets and analytical workflows. - Adhere to all ethical standards governing the conduct of research involving human biological samples and associated clinical and epidemiological data. Qualifications, skills and experience - A Master's degree in Bioinformatics, Computational Biology, Structural Biology, Molecular Biology or a closely related field with a strong quantitative component. - Demonstrated research experience in bioinformatics, computational biology, structural biology or a closely related area, as evidenced by an MSc thesis, publications or equivalent research work. - Proficiency in Python and/or R for biological data analysis, with a portfolio of applied work. - Working knowledge of Linux/Unix environments and High Performance Computing (HPC) systems. - Experience with biological sequence or structural databases (e.g. NCBI, UniProt, PlasmoDB, PDB, AlphaFold DB). - Experience with AI or machine learning tools applied to biological data, including variant effect prediction, protein structure modelling or related applications. - Understanding of malaria biology, P. falciparum genomics, antimalarial drug resistance mechanisms or vaccine antigen biology. - Experience contributing to collaborative, multidisciplinary research teams. How to apply Interested candidates should submit a single PDF document containing: (i) a research concept (maximum two pages) in line with applying Bioinformatics or Data Science to either malaria surveillance, drug resistance or vaccine design; (ii) a current curriculum vitae including publications where applicable; (iii) certified academic documents for both Bachelor's and Master's degrees; and (iv) recommendation letters from two academic referees. Only shortlisted candidates will be contacted. Deadline The listing does not specify a closing date; interested candidates are encouraged to apply as soon as possible.