Post Doctoral Fellow
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Key skills for this role
About the Role
Khalifa University seeks a Postdoctoral Fellow to develop new computational, machine learning, and AI methods for integrating and analyzing large-scale health data.
Key Skills for This Role
Responsibilities
- Develop new computational, machine learning, and statistical analysis methods for integrating and analyzing large scale data
- Develop data analysis strategies, write algorithms, and deploy computational tools for the exploration of very large data sets
- Develop a resource for the community that gives access to collected data
- Work closely with colleagues and core to understand the nature of generated data
- Actively participate in the preparation of manuscripts for publication and present at scientific conferences
- Assist in peer and student mentorship
- Share expertise and provide training and guidance to group members as needed
Requirements
- PhD in life sciences or a computational discipline, with at least 3 years of postdoctoral experience in drug discovery or biotechnology
- Strong background in computer aided drug design, including structure based and ligand based approaches, bioinformatics methods, docking, virtual screening, co folding methods, generative models, GSEA, and pathway analysis
- Proven experience in drug discovery projects
- Excellent written and verbal communication skills in English
- Ability to work effectively within a team on multiple tasks with competing deadlines and priorities
- Strong reasoning, analytical, and problem solving skills
- Experience contributing to scientific papers and journal articles, ideally as lead and/or corresponding author
Full Job Posting
Project Overview
- This project, led by Dr. Antonello Maruotti and Dr. Andreas Bender in the Center for Biotechnology, develops new computational, machine learning, and AI methods for the integration and analysis of large scale health data.
- The Postdoctoral research fellow will design data analysis strategies, implement algorithms, and deploy reproducible computational tools to explore very large datasets generated from population health studies and related sources.
- A key objective is to build a resource for the community that provides structured access to curated data and analysis pipelines, ultimately supporting evidence based public health decision making in the UAE and beyond.
Responsibilities
- Develop new computational, machine learning, and statistical analysis methods for integrating and analyzing large scale data using current state of the art technologies.
- Develop data analysis strategies, write algorithms, and deploy computational tools for the exploration of very large data sets.
- Develop a resource for the community that gives access to collected data.
- Work closely with colleagues and core to understand the nature of generated data.
- Actively participate in the preparation of manuscripts for publication and present at scientific conferences.
- Assist in peer and student mentorship.
- Share expertise and provide training and guidance to group members as needed.
Qualifications
- Strong background in computer aided drug design, including structure based and ligand based approaches, bioinformatics methods, docking, virtual screening, co folding methods, generative models, GSEA, and pathway analysis
- Proven experience in drug discovery projects
- PhD in life sciences or a computational discipline, with at least 3 years of postdoctoral experience in drug discovery or biotechnology, in academia and/or industry, supported by relevant scientific outputs such as publications and patents
- Demonstrated ability to carry out assigned research tasks while effectively managing workload and priorities
- Commitment to contributing to a collaborative and supportive team culture
- Excellent written and verbal communication skills in English
- Ability to work effectively within a team on multiple tasks with competing deadlines and priorities
- Strong reasoning, analytical, and problem solving skills
- Experience contributing to scientific papers and journal articles, ideally as lead and/or corresponding author
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