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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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for Sustainable Housing and buildings”, you will: Develop an ontology of regenerative building production by analyzing how regeneration affects on-site praxis, economic structures, and business models and
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space Collaborate with other computational researchers to build better models Work closely with experimental researchers to guide synthesis and validate computational predictions Present findings
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strengthen your application: Experience with computational ship hydrodynamics Experience with STAR-CCM+ and Abaqus Knowledge of Control Algorithms (e.g. PID controller) Knowledge of Reduced Ordered Models What
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-resolved microscopy data Collaborate with other computational researchers to build better models Collaborate with experimental researchers to validate predictions Present findings at scientific meetings and
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm
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, or relevant fields. Excellent programming skills in modern programming languages are required, as well as experience in computational or mathematical modelling. Experience with the analysis of biological data
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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materials for synthesizing different types of hydrogen storage molecules. Using advanced quantum mechanical calculations, you will develop multi-scale models to study reaction kinetics and improve catalyst
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the material. New CVD processes are mainly developed by trial-and-error. Models and computational simulations are used to improve our understanding of the complex CVD process, but there are many challenges in