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Field
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optimizing experiments, collaborating with other lab members to support complementary projects, participating actively in weekly lab meetings and research discussions (65%); • Write and publish high-quality
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(multiscale, QSP, PBPK, PK-PD).Apply numerical methods, optimization, and parameter estimation to calibrate models to experimental/clinical data.Perform sensitivity and uncertainty analyses to assess robustness
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optimizing their functional behavior. The goal of this postdoctoral project is to carry out state-of-the-art experimental research on mechanical properties of HEA NPs with the intent of elucidating the link
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modification and engineering of model and non-model microorganisms, designing, troubleshooting, and optimizing experiments, collaborating with other lab members to support complementary projects, participating
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initiatives include developing advanced aqueous emulsion and suspension systems for spray coating, predictive modeling of packaging performance, and optimizing packaging designs for high-value product
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NAnoparticles by combining experimental and numerical approaches ), funded in 2024 by ANR – The French National Research Agency. Nanoparticle (NP) synthesis is a viable way of pursuing the production of next
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-of-the-art experimental techniques, innovative methodologies, and/or numerical models to make fundamental advances to existing literature. Duties will include but are not limited to: Helping develop novel
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Science, or a related field Strong programming skills in Python, R Solid understanding of: Machine learning fundamentals Deep learning architecture (e.g., CNNs, RNNs, Transformers) Optimization and model
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[map ] Subject Area: Applied and Numerical Analysis Appl Deadline: 2025/09/15 11:59PM (posted 2025/08/05, listed until 2025/12/31) Position Description: Apply Position Description Doctoral and
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(f/m/d) full time, initially limited until 31.12.2028 Remuneration according to TV-L The Göttingen Campus is hosting numerous prestigious and internationally renowned research institutions