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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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Your Job: As a PhD candidate you will develop and deploy an artificial intelligence (AI) driven approach to streamline high-throughput experimentation (IMD-3: Institute of Energy Materials and
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Participation in the development of the institute Your Profile: Sucessfully completed scientific university degree (Master) in the fields of technical chemistry, physical chemistry or a comparable discipline
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the manufacturing and characterization of material samples. As such, the position offers the opportunity to be involved in fruitful national collaborations. In this exciting job you can expect: to develop automated
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PhD Position - Organic Electrosynthesis: monitoring of reaction transients with real-time techniques
real-time analysis of electrochemical processes, developed in the Department of Electrocatalysis, will be applied by You to discover and develop novel Organic Electrosynthetic Protocols. Your tasks
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assessment, you will develop new, sample-efficient optimal control approaches for gate calibration and test them in numerical simulations. You will pursue your research with the German research collaboration
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Profile: Required qualifications and skills: University degree (M.Sc. or equivalent) in chemistry, materials science, geoscience, physics or related field Experience in laboratory work, e.g. synthesis and
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Your Job: Maintain, and update quantitative methods for assessing economic impacts of the energy transition at the national and regional levels Develop dynamic and multisectoral economic models
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. You will join the teams of Dr. Markus Heinrich at the Institute for Theoretical Physics of the University of Cologne and of Prof. Matteo Rizzi at PGI-8 at the Forschungszentrum Jülich. You may consult
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science