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Candidate We seek an ambitious researcher with a strong foundation in Bayesian theory, machine learning, and mathematical modelling. You should be confident in software development and capable of passing a
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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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and synthesis of novel materials. Key Responsibilities: Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning. Integrate
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designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical
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network inference and modelling approaches from large omics datasets and the Sheriff team leads the development of innovative modelling approaches and maintenance of the Biomodels database. Key
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Skip to content HARVARD.EDU About Mission / Vision People Annual Reports Contact Us Programs AWS Impact Computing Bias² Causal Inference CrisisReady Fellowships & Funds SPUDS Trust in Science See
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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consumer facing tools pursue and develop a translational research program related to the evaluation of treatment modalities in fertility treatment using modern causal inference methods applied to real-world
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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Responsibilities of the Post Conduct research and development on sign language recognition using computer vision and machine learning techniques. Lead the implementation of inference models suitable for mobile and