Postdoctoral Fellow
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Key skills for this role
About the Role
The Department of Genetics and Genomics at UAE University is seeking a Postdoctoral researcher to apply AI, ML, and NLP techniques to diagnose rare diseases using EHR data. Candidates must have a PhD in a quantitative field, strong Python skills, and a proven publication record.
Key Skills for This Role
Responsibilities
- Conduct advanced, independent, and collaborative research in ML, NLP, and LLMs focused on rare disease diagnosis
- Design, implement, and evaluate novel ML models, including LLMs and NLP techniques, customized for clinical datasets
- Work closely with a collaborative team of PhD students and Research Assistants, providing technical guidance and mentorship
- Lead and participate in planning, designing, and executing research experiments with robust statistical validation
- Translate research outcomes into high impact publications and present findings at major international conferences
- Assist faculty members in drafting competitive research grant proposals
Requirements
- PhD in Artificial Intelligence, Data Science, Computer Science, Bioinformatics, or a closely related quantitative field
- Proven track record of research productivity demonstrated by first author publications in peer reviewed journals or top tier conferences
- Strong foundational and practical knowledge of AI, ML, and NLP techniques
- Proven proficiency in Python programming and experience with data science frameworks
- Experience with LLMs, NLP, or multimodal data integration is highly preferred
- Excellent analytical, algorithmic, and problem solving skills
- Strong communication skills in English
Full Job Posting
Job Description
- The Department of Genetics and Genomics at the College of Medicine and Health Sciences, UAEU, is looking for a highly skilled, motivated, and collaborative Postdoctoral researcher.
- This position focuses on leveraging advanced AI, ML, and NLP techniques to advance research into diagnosing rare diseases using Electronic Health Record (EHR) data.
Minimum Qualification
- PhD in Artificial Intelligence, Data Science, Computer Science, Bioinformatics, or a closely related quantitative field.
- Proven track record of research productivity demonstrated by first author publications in peer reviewed journals or top tier conferences (e.g., NeurIPS, CVPR).
- Strong foundational and practical knowledge of AI, ML, and NLP techniques.
- Proven proficiency in Python programming and experience with data science frameworks.
- Experience with LLMs, NLP, or multimodal data integration is highly preferred.
Expected Skills
- Excellent analytical, algorithmic, and problem solving skills.
- Strong communication skills (both written and verbal) in English, with the ability to present complex technical ideas to clinical communities.
- Demonstrated leadership and interpersonal skills, with a track record of mentoring junior researchers or graduate students.
- Proven ability to work independently, manage multiple priorities, and meet project milestones within a one year timeline.
- High level of organizational skill and focused attention to research details.
Responsibilities
- Conduct advanced, independent, and collaborative research in ML, NLP, and LLMs with a dedicated focus on rare disease diagnosis.
- Design, implement, and evaluate novel ML models, including LLMs and NLP techniques, customized for clinical datasets.
- Work closely with a collaborative team of PhD students and Research Assistants (RAs), providing technical guidance, hands on mentorship, and project co supervision.
- Lead and participate in the planning, designing, and executing of research experiments, ensuring robust statistical validation and analysis of clinical data.
- Translate research outcomes into high impact publications for top tier, peer reviewed journals and present findings at major international conferences.
- Assist faculty members in drafting competitive research grant proposals to secure external funding.
- Actively participate in academic and departmental activities, including seminars, workshops, journal clubs, and cross institutional collaborations.
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