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optimization or related inverse design techniques. While this position does not involve developing AI models, it requires close collaboration with AI researchers to ensure data is appropriately structured for AI
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in topology optimization or related inverse design techniques. While this position does not involve developing AI models, it requires close collaboration with AI researchers to ensure data is
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in English in one PDF file. The file must include: Application (cover letter) Full CV Academic Diplomas (MSc & PhD – in English) List of publications incl. link to Google Scholar profile Link to Github
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the position, and preferred start date. Curriculum vitae (including a complete publication list). Contact information for 2-3 references. Stanford is an equal opportunity employer and all qualified applicants
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upload copies of your transcripts or grades. Please note that all documentation must be in English. General information The best qualified candidates will invited for interviews. Applicant lists can be
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data generated by super-resolution STED microscopy, FLIM, FRET and FCS. Candidates must hold a PhD in cell biology, biophysics or biochemistry along with experience in advance quantitative microscopy
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recruitment portal (link Apply now or Employee login in the job announcement). Please include the following in your application: Motivational letter outlining why you are the right person for this task (max 1
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. You can read more about career paths at DTU here . Further information Further information may be obtained from Professor Anker Degn Jensen: +45 2217 1723, e-mail aj@kt.dtu.dk . If you are applying from
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fermentation and formulation methods for whole-cell BioAg products. A central focus will be linking production processes with field performance by studying physiological, chemical, and molecular-genetic
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functional screens and read out mutation identities via barcode sequencing. By providing a direct link between sequence and function MAGESTIC enables massively scaled screens of precise mutations, with