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of limited resourcing is desirable have experience in a leadership position (desirable) For further information about the position, please contact Dean Marianne Holmer at +45 60112605. First interviews are
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. We expect applicants to hold a PhD in a relevant field such as techno-anthropology, science and technology studies, human-computer interaction, human-robot interaction, digital health, anthropology
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supportive measures and activities for student life Your profile: Proficient in English, ideally in Danish and German Outgoing and conversational Highly organized Contact information For more information about
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Faculty position in Molecular Health (Tenure-Track Assistant Professor / Associate Professor) at ...
employment is Aarhus University with related departments. Contact information For further information, please contact: Head of Department, Claus Oxvig. Phone number: +45 30362460 Email: co@mbg.au.dk Deadline
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agreed upon with the relevant union. The period of employment is 2 years. You can read more about career paths at DTU here . Further information Further information may be obtained from Antonio Grimalt
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research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and
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studies and are flexible around your working days. Contact: For further information about the position, please contact Krzysztof Sierszecki at email: krzys(snabel-a)mmmi.sdu.dk . Important information
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across career stages Starting date: April 1st 2026, or soon after Applicants seeking further information can contact: Professor Amelia-Elena Rotaru arotaruATbiology.sdu.dk . Who are we? The successful
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metabolic data and correlate NMR readouts with physiological function. Preferred Qualifications: PhD in Bioengineering, Chemistry, Biophysics, or a related field. Extensive hands-on experience with organ-on-a
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will