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alongside residents and faculty Support for conference travel to present research Preferred—but not required—experience includes backgrounds in: 3D Printing Engineering Computer Aided Design Medical
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Sociology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 23 Mar 2026 - 23:59 (Europe/Lisbon) Country Portugal Type of Contract Other Type of Contract Extra
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profile The successful candidate should have a PhD degree in physics or engineering and experience in nano-lithography, optical instrumentation and metrology, or synchrotron radiation Knowledge in
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. The position is placed in the Digital Signal Processing and Image Analysis group (DSB), Section for Machine Learning, Department of Informatics at the University of Oslo. The DSB research group has seven full
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-organization during pre-implantation development, in close connection with experimental data from live imaging and spatially resolved gene expression profiling. The work of the PhD fellow will be theoretical and
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of Engineering and contribute to industry-engaged research in construction materials and modelling. This role offers a rare opportunity to work across laboratory experimentation, scaled-up prototype testing, and
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. QUALIFICATIONS Successful applicants will have: PhD in Biomedical Engineering, Ultrasonics, Medical Imaging, Applied Physics, or related field Experience with imbedded hardware, signal processing, and programming
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, Biological Engineering, Bioengineering, Biomedical Engineering, Analytical Chemistry, Micro-technology, Microengineering, Biotechnology. Application procedure The call for applications is open until the 25th
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. Preferential factors: • Education or training in reaction engineering, microfluidics, microbiology, fluid dynamics, image analysis, or machine learning (where applicable to the specific PhD project); • Fluent
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess