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Field
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interpretation of OMICs datasets (DNA, RNA, proteome) Supervision and technical guidance of Bachelor’s, Master’s, and PhD students, as well as technical staff within the project context Participation in teaching
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electronic health records (EHRs) from multiple UK hospital centres using advanced data analytics including machine learning, deep learning, and statistical techniques—with a particular emphasis on deep
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. Qualifications Required Qualifications: A PhD degree in Biostatistics, Statistics, Computer Science or a related field who possesses strong interest in genetics/biomedical applications, and strong computing
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Agency (ARIA). The PROTECT project (Probabilistic Forecasting of Climate Tipping Points) brings together cutting-edge AI, statistical, and machine learning techniques with climate modelling, aiming
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-motivated, creative, productive, and organized. Successful candidates should possess expertise in statistical programming languages such as SAS, Stata and/or R. There is funding available for these positions
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biomarkers. The successful candidate must have strong grounding in statistics. This position will focus on intensive research training, professional development and mentorship of undergraduate and graduate
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and relevant to research agendas. Additional duties may be assigned. Minimum Qualifications Must have completed all the requirements for a PhD. in Astronomy, Physics, Astrophysics, or related science at
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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disseminating results via conferences, talks, and first-author manuscripts. Experience in some of these methodologies is required. Qualifications: Applicants should have a PhD degree (or PhD candidates in
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– Abilities Biology, Chemistry, Mathematics, and Statistics. Documentation and records management Data utilization Complex problem solving and critical thinking Resource management Writing skills Preferred