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. This includes exploring the use of digital twins for bioreactors and deploying AI driven predictive models to improve optimisation, consistency and overall yield. The main focus for this role is to work with the
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their impact on gene expression. Contribute to large-scale modeling of engineered traits to predict performance and optimize design. Required Qualifications: PhD in the field of genomics, evolution, population
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pipelines that push technological boundaries for our clients. The Engineer will tackle complex challenges at the intersection of Large Language Models, Computer Vision, and Predictive Analytics while ensuring
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) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane intelligence (e.g., reinforcement learning for scheduling
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Control - Resilient autonomy for Self-Healing Soft Robotic Platforms - Uncertainty-Aware and Predictive Human-Robot Interaction Qualifications To be qualified for the position, you must have a MSc degree in
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and increased uncertainty in life and non-life insurance modelling. data-driven prediction of insurance premiums and associated quantification of uncertainty. Qualifications and personal qualities
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geometries and process-induced defects demand new inspection approaches. The project combines modelling, sensor fabrication, experiment, and data analysis. You will work with a team of experts to develop
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 2 months ago
? No Offer Description Scientific overview: This PhD thesis aims at studying the impacts of climate change-induced permafrost thaw in the Arctic, by using advanced thermo-hydro-mechanical (THM) modelling
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with The School of Natural Sciences and the Discipline of Geology, seek to appoint an AIB/E3 Assistant Professor in the area of Earth System Modelling. More specifically, the successful candidate will utilize
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areas providing a template for relevant directions: - Embodied Intelligence for Soft Robotic Systems - Foundational Models for Adaptive Soft Robots - Real-Time Adaptive and Stiffness-Aware Control