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://chiralnanomat.eu/ we offer two PhD positions: DC9 - Bio-Functionalized Chiral Nanocluster Modelling via Machine-Learning Methods DC10 - Predictive Modelling and Rational Design of Asymmetric Catalysis by Chiral
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience
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of computational mechanics, mechanical and civil engineering, and scientific machine learning. The postdoc will be encouraged to publish in leading international journals, present their work at major conferences
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application! We are looking for a research engineer within the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science. In this position, you will have the
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Infrastructure? No Offer Description Work group: IAS-8 - Datenanalyik und Maschinenlernen Area of research: PHD Thesis Job description: Your Job: We are looking for a PhD student in machine learning to work within
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materials. Where to apply Website https://www.academictransfer.com/en/jobs/357941/phd-position-in-explainable-ai-… Requirements Specific Requirements We are looking for a collaborative and aspirational new
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https://engineering.purdue.edu/PMRI). The population of officers at Purdue currently exceeds 100 students pursuing PhDs and MS degrees. We intent to grow this number to build a population of unique
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the Computer Engineering program . We seek candidates with a research focus on the theoretical foundations and practical applications of advanced machine learning techniques. Emphasis will be given
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such as VASP, Quantum ESPRESSO, LAMMPS, GROMACS. • Machine-learned interatomic potentials. • Structure-property prediction using GNNs. • LLM fine-tuning and prompt engineering (e.g., HuggingFace, OpenAI
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship