16 phd-in-architecture-interior-design-built-environment Postdoctoral positions at University of Lund
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forests and marine environment and pest surveillance in aquafarming. Our group will comprise a handful of PhD candidates, and several researchers and MSc students and also a broad interdisciplinary network
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Description of the workplace The position is located at the division of Packaging Logistics at the Department of Design Sciences at LTH, Lund University in collaboration with the division
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around 15 are PhD students. The work environment is open and welcoming, striving to provide each employee with the opportunity to develop personally and professionally. The field of solid mechanics relates
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molecular questions in biological systems, and study biological and designed proteins using a combination of classical and advanced techniques including optical and NMR spectroscopy, surface plasmon resonance
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, bacteriophages. Prof. Hauryliuk obtained his PhD in 2008 at Uppsala University, Sweden. His scientific contributions were recognized though the Ragnar Söderberg fellowship in Medicine (2014), the Swedish Fernström
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evaluation process etc please visit: ambercofund.eu. Qualifications Minimum requirements are: Candidate needs to have a maximum 8 years after a doctoral degree (PhD), as required by the project Grant Agreement
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(including shifts during the night). Eligibility and Qualification requirements To be eligible for the posistion the candidate must have a doctoral degree (PhD) no older than 8 years, as required by
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well as developing methods to study this process. Within the research group, we prioritize a positive work environment characterized by respect and consideration in our interactions with one another. We strive
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well as structural dynamics plays a central role. Approximately 30 colleagues work at the division, including 20 PhD students. On the international level we collaborate with universities and institutes in Europe, Asia
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman