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experience (front-/back-end, metrology/inspection, equipment maintenance, yield-improvement projects). Digital twin, IIoT, MLOps, real-time data streaming, edge computing. Causal/XAI, Bayesian methods
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varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference
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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set
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chemistry concepts (desirable). Familiarity with chemical or biological databases (e.g., ChEMBL, PubChem, PDB) is a plus. Experience with Bayesian modelling, transfer learning, few-shot learning, or other
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programming techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference
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to conduct one's own postgraduate education. This includes, in addition to completing mandatory doctoral courses, using sequence analysis (including phylogenetics [both maximum-likelihood and Bayesian analysis
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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree
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Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine