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-control vs cohort, etc.). Strong grasp of statistical/ epidemiological principles e.g. risk prediction and survival/time-to-event modelling. Experience with uncertainty aware evaluation: confidence
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of antibiotic resistance. You will build generative protein models to predict plausible future resistance mutations, use these models to guide high-throughput experimental screens of millions of enzyme variants
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of artificial intelligence, multi-omics data integration, and functional genomics, aimed at predicting synthetic lethality in cancer - including representation learning, nonlinear embeddings, and predictive
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evaluation of predictive models and statistical approaches to understand treatment outcomes. Integration of diverse data types to identify features associated with therapeutic response, resistance, or toxicity
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the development of the first predictive model for T-cell immunogenicity of HLA class II-presented peptides in the context of protein therapeutics. If you're looking to launch your scientific career
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the Finnish Center of Excellence in Quantum Materials . Your role and goals The research will focus on developing and using machine learning algorithms to discover novel materials and to build generative models
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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modern workflow managers (e.g. Nextflow, Snakemake) and version control; support for novel wet-lab protocols for DNA methylation analysis and nanopore sequencing; and development of predictive models
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& Systems Biology program at MSK. Our long-term goal is to enable rational engineering of cell state: using large-scale functional genomics to build predictive models of how cells respond to perturbations
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of artificial intelligence, multi-omics data integration, and functional genomics, aimed at predicting synthetic lethality in cancer - including representation learning, nonlinear embeddings, and predictive