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state-of-the-art machine learning/deep learning models for RNA sequence design, structure prediction, and property optimization. Process, curate, and model high-throughput screening and genomics
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, to carry out the first well-powered genetic studies of these neonatal conditions. Analysis of new sequence and genotype data from over 110,000 individuals across four birth cohorts, including the
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, sequence alignment, and annotation. Strong quantitative and statistical analysis skills for experimental data interpretation. Rigorous scientific documentation practices, including maintenance of
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education courses. Applicants should be prepared 1) to identify and guide students through fundamental questions in a Core sequence and 2) to contribute meaningfully to the instructional community and
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representation learning, dynamical systems, sequence generative models and world modelling. Emphasis will be on methods that design and implement new architectures for latent dynamic modelling to
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statistical methods to study reaction variables dependence on oligonucleotide sequence and concentration, enzyme concentrations, buffer composition, temperature, incubation time and other relevant
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, provide intellectual leadership across multiple concurrent grants, and mentor junior researchers. The portfolio spans methodological innovation and applied public health impact, including real-time
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genetic manipulations to study sensory processing and motor sequence generation. Analyse experimental data, maintain reproducible research records and interpret findings in relation to neural circuit
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. Computational cognitive models and modern deep learning are our main instruments for this work. We treat deep reinforcement learning agents and sequence models as scientific models of human behavior. We
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discipline. Strong background in machine learning/data science, with hands-on Python and experience with modern ML frameworks. Experience with one or more of time-series/sequence modelling, state estimation