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Field
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are currently seeking a highly motivated PhD student to join our team to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the successful
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optimization algorithms, you will design structures that deliberately harness modal couplings to exhibit tailored nonlinear behaviour, with direct applications in ultrasensitive resonant sensing. Together
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the second direction, you will explore the geometric design of nonlinear systems. Using nonlinear reduced order modelling (ROM) integrated with optimization algorithms, you will design structures
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several phases, including analyzing the blind spots of language models, identifying challenges specific to the police context, and optimizing existing AI solutions for secure use within law enforcement. Job
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training. Generative AI model training, Design and implement generative models such as Generative Adversarial Networks, Variational Autoencoders, and Diffusion Models to generate realistic ship designs
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training. Generative AI model training, Design and implement generative models such as Generative Adversarial Networks, Variational Autoencoders, and Diffusion Models to generate realistic ship designs
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(TU Delft). Characterizing powder morphology, flowability, oxidation, and contamination. Fabricating parts using LPBF printing, optimizing process parameters for recycled feedstock. Benchmarking
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the risks of business case development. A strong collaboration with consortium members in industry and academia is key. Research Context The EU aims to have a net-zero greenhouse gas (GHG) economy by 2050
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and optimize device architectures using finite element simulations. Fabricate prototypes using 3D printing, and cleanroom technologies. Implement test setups and assess devices performance through
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strategies (e.g. predictive or machine learning approaches) to improve performance and reduce costs. Collaborating with industrial partners on design optimization, life-cycle analysis, and business case