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methodologies. Understanding of integrating Bayesian approaches in NN-based model Knowledge of model deployments to cloud platforms or past work with AutoML tools. Knowledge of MLFlow for maintaining model
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Review, update, and consolidate methodologies, including Bayesian methodologies, in the context of material balance evaluation Your Profile: PhD in applied mathematics, computer science, physics, or in
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the complex development phases of new production processes faster, more cost-effective and efficient through the targeted use of AI methods. What you will do Your research will be mainly related with two sub
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to identify and promote the voluntary adoption of effective bycatch reduction strategies in targeted fisheries. There will also be opportunities to contribute to writing research proposals and mentoring
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA
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, automation, and multi-parameter optimization, and create a closed loop pipeline for the rapid design of protein-based binders to any target and simultaneously optimized for developability and manufacturability