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Field
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data to design robust, efficient deep learning algorithms that maximize the information extracted from images and delivered to the robot. To be successful in this role, we are looking for candidates
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functioning in temperate grasslands. In addition, you will design and implement a factorial experiment testing the potential of trait-based approaches to create positive priority effects across diverse
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(based on biomaterials and living cells), including their efficient deployment on embedded hardware, as well as the design and management of the supporting data pipeline. You will be part of
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position is part of MODABAT – Modular, Scalable, and Technology-Open Design for Future Aviation Batteries, a project that aims to develop an airworthy battery system for an ultra-efficient regional aircraft
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to work on improving their generalizability and robustness by exploring different modelling techniques, evaluating the impact of design choices, and addressing common pitfalls in model interpretation with
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crop breeding by design. You will build models, analyse new sequencing data and optimise strategies for combining multiple advantageous traits. Contribute to groundbreaking research shaping the future
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Prognostics & Health Management for Future Aviation Batteries Job description This postdoctoral position is part of MODABAT – Modular, Scalable, and Technology-Open Design for Future Aviation
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prediction of BESS’s electric and thermal behaviours. Optimization of BESS design for high energy density, durability and safety. Validation of models by benchmarking with cell and system level measurements
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of Earth observation (EO) by embracing disruptive innovation and acting as a catalyst for disruptive/transformative innovation in the sector. Our vision is to become an “EO innovation hub” connecting EO with
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batteries (solid-state) via a physics-informed data-driven approach. Accurate prediction of BESS’s electric and thermal behaviours. Optimization of BESS design for high energy density, durability and safety