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The position is funded by the Open Philanthropy grant “Verifiably Robust Conformal Probes”. The project’s goal is to develop methods for latent probing (aka activation monitoring) of large language models (LLMs
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following skills and experience: Essential criteria PhD in statistical/psychiatric/behavioural genetics or a related academic area with a strong data analysis component Excellent coding skills with a focus on
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Certificate Examination (SCE) Proven competency in Python, R, and Stata coding Experience in systematic reviews and meta-analyses, including Bayesian methods Clinical experience in rheumatology, including
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systematic reviews and meta-analyses, including Bayesian methods Clinical experience in rheumatology, including outpatient clinics Desirable Criteria: Higher degree (MD/PhD) in a relevant field Record of peer
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Completed core medical training and currently in higher specialty training Interest in translational cancer research Understanding of clinical research methods and GCP Experience in academic/research
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Evaluation (CMHPE), utilising their mixed methods their mixed-methods (ideally qualitative and quantitative) expertise. The role includes planning, coordinating, executing, and reporting on evaluation and
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for this role. This role will involve developing and applying analysis plans using a variety of advanced methods with the support of project supervisors. The postholder will have completed a PhD in a relevant
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of genome organisation and metabolic control - with the bold vision of building synthetic life. In this role, you will develop and apply deep learning methods to analyse single-cell modalities, focusing
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-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological insights that drive our translational and fundamental research programmes. In
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spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological