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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 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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. Weinstein (DTU Chemistry) to develop novel molecular ML methods and use them to control, steer, and learn from large scale wetlab experiments. You will deploy these methods in collaboration with Prof. Katrine
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. Responsibilities and qualifications Guided by initial AI predictions from the Weinstein group and microbiology results from the Tolker-Nielsen Lab, your main responsibilities will be to design, synthesize and
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will also develop methodology to produce synthetic control cohorts for comparative assessments against novel therapeutics evaluated in clinical trials. The project will interface with clinical and
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highly talented and motivated Postdoc candidate within the field of Immunoinformatics and prediction of T cell immunogenicity. HLA class II antigen presentation is fundamental to T-helper cell
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capable of breakthroughs. The research will mix state of the art numerical skills with analytic understanding. Your task is to predict new classes of materials that have not been considered before, beyond
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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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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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a team of 3 postdocs and 3 PhD students, who will work on design, characterization, and experimental verification of several RNA systems that can regulate gene expression, control enzyme proximity