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Field
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Language Model (LLM) Strong biostatistics knowledge including survival analysis and causal inference Experience with reinforcement learning, agentic AI systems and autonomous decision-making frameworks Data
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techniques for antigen and antibody detection. A good understanding of bioinformatic and biostatistical approaches to data analysis and of TCR and antibody characterisation to be used for T cell engineering
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a PhD in Computational Biology, Applied Mathematics, Statistics, Biostatistics, Epidemiology, Bioinformatics, Computer Science, Neurological Genetics or a closely related discipline. Excellent oral
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, epidemiology, statistical genetics, bioinformatics, biostatistics, computer science, or a related field. Statistical and programming skills, such as R and the use of UNIX/LINUX, are required. Experience in
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, biostatistics, public health, with a strong component in epidemiology and biostatistics, or a related degree Research experience in terms of publications in high quality journals, attraction of external
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sections from bioinformatic data analyses. Qualifications Degree in Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related field. Previous experience in
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metrics and advancing its research and methodologies. Ideal candidates will bring expertise from fields such as public health, epidemiology, biostatistics, economics, or quantitative social sciences, and
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novel environmental change and health research from conception to output stages, including relevant expertise in intervention studies, environmental epidemiology, biostatistics, or another related
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Services and Policy; Epidemiology, Biostatistics and Public Health Practice; and Health in Populations. The resulting mix of professions and disciplines is seen as a means of connecting individuals and
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epidemiology, biostatistics and health economics. You will also have a unique opportunity to develop your clinical experience and expertise in the management of adults with rare bone disorders from the clinics