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Field
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modeling and analysis. Ability to select, implement, diagnose, and adapt parameter-estimation or statistical-inference methods to suit the model, data structure, and scientific question. Experience with
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error estimate, and a full record of how it was computed. Build and scale MLIP/MD/DFT workflows (e.g., atomate2, MACE/CHGNet/UMA-class potentials) to hundreds of compositions on HPC. Validate predictions
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-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB, R, or related computational environments. Lead and co-author
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fitting, parameter estimation, and model comparison Proficiency in at least one scientific programming language (e.g., Python, MATLAB, R) Demonstrated ability to work independently while collaborating
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modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model validation. Experience or strong interest in scientific AI/ML, including surrogate or multi
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performance and detect anomalies Develop AI-driven predictive maintenance strategies to anticipate system failures or performance degradation Use AI/ML methods to optimize thermal system design parameters and
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-orientational metrics such as the q₃ parameter, correlate with electronic transport, optical response and thermal behaviour. Requested to have expertise in device simulation, the researcher will also study
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quality and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how physical and chemical fiber parameters relate to dye uptake behavior, dyebath exhaustion, color
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control probe positioning and ultrasound imaging parameters. 4, Design and test intuitive human-machine interfaces to support non-specialist users in operating wearable ultrasound devices. Conduct
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process modeling, simulation, and systems analysis Experience with experimental work in chemical or process engineering (desirable) Knowledge of process optimization, parameter estimation, or control