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. The candidate will collaborate closely with team members to develop novel quantum computing algorithms that leverage hybrid detection methods and explore the potential of 3D cluster states for enabling fault
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, glacier speedup, and ice-ocean interaction. Candidates will work with satellite altimetry, velocity datasets, and climate data to quantify ice sheet mass balance and dynamics. Applicants should hold a PhD
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experiments To cooperate with project partners (researchers and industries) to identify key issues that must be solved to improve recycling of materials in the industry To communicate scientific results, orally
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also represents an underutilised carbon feedstock. Simultaneously, microplastics and persistent organic micropollutants (e.g., PFAS, pharmaceuticals) are increasingly detected in water bodies, posing
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capabilities are required for writing documentation and for communication with colleagues in a multinational setting As a formal qualification, you must hold a PhD degree (or equivalent). We offer DTU is a
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. Find more information on “Enzyme Engineering Group” here . Qualifications Completed PhD in the field of Biochemistry, Molecular Biology, Biotechnology or relevant field. Strong background in enzymology
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scientific journals. Your profile We are searching for a highly motivated candidate who has A PhD in remote sensing, data science, agriculture, environmental sciences or similar Collaborative skills and
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for extinction risk prediction Qualifications We are looking for a candidate who has: As a formal qualification, you must hold a PhD degree (or equivalent) in bioinformatics, computational biology, evolutionary
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be employed by the Department of Biology at Aarhus University and work in the Section for Microbiology at this department. The section employs 12 permanent scientific staff and ~20 PhD students and
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involves conducting research into innovative electromechanical robotic systems integrated with advanced sensing technologies, enabling the autonomous detection, interaction with, and replacement of small