55 web-programmer-developer-university-of-liverpool PhD positions at Forschungszentrum Jülich
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for personal and professional further development. A structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral
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, interdisciplinary research environment at the forefront of materials informatics Comprehensive training courses and individual opportunities for personal and professional further development. A structured program of
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further development. A structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors
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research position in a dynamic and collaborative environment Extensive opportunities for personal development and training, e.g. through an extensive range of training courses; a structured program of
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, offering the best possible means for networking with colleagues and pursuing sports alongside work Opportunity to develop your strengths, e.g. through a comprehensive training programme; a structured
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of structure-performance relationships of different catalyst and membrane surfaces Development and validation of various test stand modifications Coordination with internal and external project partners from
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holidays and weekends (e.g. between Christmas and New Year) Flexible working hours Further development of your personal strengths, e.g. through an extensive range of training courses; a structured program of
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: Develop an event-driven RL algorithm that sparsely updates network state and parameters that will significantly improve energy to-solution efficiency compared to conventional digital accelerators when
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Jülich - Participation in the development of the institute Your Profile: Sucessfully completed scientific university degree (Master) in the fields of chemical engineering, process engineering, chemistry
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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