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Description of the workplace Medical Radiation Physics is an interdisciplinary subject, which is directed towards development and discovery of new knowledge for applications of radiation and
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universities worldwide. We are looking for a PhD student in Process Metallurgy who will conduct research studies in the field of iron oxide reduction and carburization using alternative hydrogen carriers. We
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) Computer Science/ Mathematics/Physics and at the second cycle level, 60 credits in Life Science, Computer Science Mathematics, Physics or Bioinformatics including a 30 credit Degree Project (thesis). Additional
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performance. Funded by the Swedish Research Council, this research is hosted at the Division of Chemical Physics. About us The research at the division of Chemical Physics at Chalmers University of Technology
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designed work schedules can contribute to traffic accidents leading to injuries or fatalities. Everyday driving in suboptimal static postures may also lead to musculoskeletal problems. Discomfort can
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mechanical analysis, nanonindentation and atomic force mircroscopy will be used to characterize the mechanical properties of (doped) conjugated polymers. You will work closely with fellow PhD students and
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. These positions involve advanced numerical simulations and analysis of spacecraft data. The positions are full-time, 100% funded for four years and lead to a doctoral degree in Computational Physics. The expected
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spectroscopies, as well as molecular biology for introducing site specific alterations. Atomic level structural insight will be obtained via single particle analysis cryo-EM and snapshot serial crystallography
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leadership roles (personnel and/or projects) in an academic or industrial environment with good references. Proven ability to combine the role of a technical expert with the ability to lead an organization and
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. The student will work in a group addressing all these challenges, developing new AI-based methods to improve biological realism in simulations which will lead to more accurately inferred GRNs from real data