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The position is a temporary full-time employment for two years with the possibility of a one-year extension. The position requires physical presence throughout the entire employment. A valid residence permit
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the possibility of a one-year extension. The position requires physical presence throughout the entire employment. A valid residence permit must be presented by the start date, otherwise the offer may be withdrawn
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engineering Engineering » Other Physics » Thermodynamics Technology » Energy technology Researcher Profile Recognised Researcher (R2) Country Sweden Application Deadline 30 Sep 2025 - 21:59 (UTC) Type
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For information about processing of personal data in the recruitment process. It may be the case that a position at KTH is classified as a security-sensitive role in accordance with the Protective Security Act
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biological systems and processes at the molecular level. The focus is on experimental and theoretical studies of the physical properties of proteins in solution. We have state of the art facilities for protein
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. Application process AnapplicationmustcontainthefollowingdocumentsinEnglishorSwedish: A complete curriculum vitae, including date of the thesis defence, title of the thesis, previous academic positions, academic
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leave, sick leave, or military service. Contract terms The position is a temporary full-time employment for two years with the possibility of a one-year extension. The position requires physical presence
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://uu.se/om-uu/jobba-hos-oss/ The position may be subject to security vetting. If security vetting is conducted, the applicant must pass the vetting process to be eligible for employment. Please do not send
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. Others For information about processing of personal data in the recruitment process. It may be the case that a position at KTH is classified as a security-sensitive role in accordance with the Protective
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, computer science, physics, materials science, or related fields. You must have proven expertise in at least one of the following fields: computational geometry, algorithm development, machine learning