68 phd-computer-engineering Postdoctoral positions at Technical University of Denmark in Denmark
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during the project period in courses at DTU. Candidates should have a two-year master's degree and as a formal qualification, you must hold a PhD degree (or equivalent) preferably culturing basidiomycetes
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and qualifications The successful candidates must have a PhD in physics, chemistry, chemical engineering, materials science or related areas as well as experience in the general area of catalysis
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materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 300
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, on the technologies and workflows of the Biosolutions Development Pipeline. Teaching will align with your area of expertise. We further expect you to contribute to supervising student projects at the BSc, MSc, and PhD
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amino acids, e.g. lysine, using fermentation technology. For this purpose, microorganisms that efficiently secrete amino acids will be developed. The vision is that microorganisms, in the future, can be
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members including other synthetic chemists, protein biochemists and the proteomics and metabolomics core facilities. You should have a PhD in protein biochemistry and biophysics, with experience in
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academic and non-technical publications. Communicate insights and findings with practitioners and stakeholders. Qualifications: We are looking for candidates with: A master’s degree and a PhD (or submission
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in working with environmental sampling at Danish utilities. Applicants can have a background in chemistry, environmental sciences and engineering, or related fields, who have practical experience from
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sensors, data collection, and data management Collaborate with interdisciplinary teams and supervise student projects As a formal qualification, you must hold a PhD degree (or equivalent). You are also
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will benefit from Lonza’s expertise and technology within peptide T cell immunogenicity, and the vast expertise within immunoinformatics and machine learning models at DTU to address this challenge