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- cellumation GmbH
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for information-driven experiment steering (closed-loop control) Work in an interdisciplinary team of engineers, computer scientists, and life scientists Present your work at international conferences and learn
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-time full-time Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline Any time Tuition fees per semester in EUR None Combined Master's degree / PhD programme
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personal computer and Internet access. Funding opportunities within the university Yes Description of the above-mentioned funding opportunities within the university PhD positions are paid and will cover all
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Additional information on beginning, duration and mode of study The starting date of the PhD project is flexible. The initial funding period is usually three years. Find out more at https://www.bgc-jena.mpg.de
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science. The PhD students are supervised by a supervisory panel consisting of a panel chair, a professor from the natural sciences and a professor from information/computer/mathematical science. The DASHH graduate
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11 Mar 2026 Job Information Organisation/Company cellumation GmbH Department HR Research Field Engineering » Computer engineering Engineering » Mechanical engineering Researcher Profile First Stage
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group has used their expertise in computer simulations on small model chromosomes to demonstrate that polymer-assisted condensates are capable of maintaining the epigenetic state through 40 generations
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deadline The application deadline has not yet been determined. Please check our website (www.ec2-big-nse.de ). Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree
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. Programme duration 6 semesters Beginning Winter semester Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
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interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state-of-the-art machine learning and