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Field
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—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive
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, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy management, or production scheduling. Good knowledge of integrated energy
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modeling and digital twins, and predictive analysis for environmental and health applications. We are committed to recruiting and retaining highly motivated, creative researchers, postdoctoral fellows, and
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implementation or comparable tools. Experience with one or more relevant methods, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy
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) understanding and controlling their underlying specific functionalities and the structure-properties relationship acting over multiple length scales from the molecular, nano to the macro level and their cross
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tissue electrical polarisation influence cell–matrix interactions during lung fibrosis progression. The postholder will develop bioelectronic in vitro platforms to precisely control matrix deformation and
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command of English. The candidate must possess a strong publication record that demonstrate their capacity of using bioinformatic platforms in life science research. The candidate should be proficient in
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predictions. Position Status Full Time Posting Number 26FA0747 Posting Open Date 09/04/2026 Posting Close Date 10/05/2026 Qualifications Minimum Education and Experience Ph.D. in life sciences, chemical
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predictions. Position Status Full Time Posting Number 26FA0747 Posting Open Date 09/04/2026 Posting Close Date Qualifications Minimum Education and Experience Ph.D. in life sciences, chemical sciences or other
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related discipline. They should be responsible, self-motivated, attentive to detail, and a strong command of English. The candidate must possess a strong publication record that demonstrate their capacity