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Development Fund (FDCT). Academic staff of IME will be allocated to the Department of Electrical and Computer Engineering, Faculty of Science and Technology (FST-ECE) as home unit. Academic staff can conduct
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use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for microscopy image analysis are especially relevant. Include a link
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potential and rapid career development opportunities. Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/134887 Requirements Specific Requirements Profile Master’s degree/Engineer
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deadlines. • Ability to work independently, and to be an accomplished team player. Preferred Qualifications • MS or PhD in chemistry, physics, materials science, engineering, or a related physical science or
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | about 2 months ago
, including soil organic matter analyses and 3D image processing. - Programming skills in R or Python to automate image analysis, build a classification, with the aim of making the reference framework
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and articulates the role of the college in an R1 institution and a vibrant student-life community. Minimum Qualifications All Ranks:· A terminal degree (MFA, PhD or EdD) from an accredited institution
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science » Computer systems Computer science » Modelling tools Computer science » Informatics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 1 May 2026 - 23:00 (Europe/Paris
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and Experience Requirements PhD degree or equivalent in Electrical or Computer Engineering or a related area. Three to five years of teaching and/or industry related experience in the fields
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Portal https://securemail.tu-dresden.de by sending it as a single pdf file to uwe.hampel at tu-dresden.de or to: TU Dresden, Chair of Imaging Techniques in Energy and Process Engineering, Prof. Dr
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented