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); - Experience with data balancing techniques; - Desirable basic knowledge of Multiple Input, Multiple Output (MIMO), Fluid Antenna System (FAS), and Compressive Sensing (CS); - PhD degree obtained within the last
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optimization in distributed systems. The work also involves modern compiler infrastructures, with emphasis on MLIR, and contributions to LLVM and the OpenMP standard. Applicants must hold a PhD in Computer
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must be sent to sinbelangero@gmail.com , with a copy to pgtunifesp@gmail.com , with the subject line: “Application for PD Fellowship - BHRC - Name + Surname.” Requirements: PhD in health or biological
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potential in preclinical experimental models. Requirements: Applicants must have obtained their PhD within the last five years and have experience in the study of non-conventional lymphocyte populations, as
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Educational background and field of knowledge: Agricultural Engineering/Agronomy or related fields, with a focus on Plant Pathology. Specific Requirements The candidate must hold a PhD degree with a thesis
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, Pharmacy, or related fields; • PhD in Medicine, Sciences, Psychobiology, or related areas; • Strong background in Physiology, Reproduction, and/or Microbiology; • Experience in clinical and preclinical
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/axonopathies.” Prerequisites: • PhD degree in Biological Sciences or Health Sciences; • Experience in techniques such as Histology, Molecular and Cell Biology, Immunohistochemistry, miRNA and RNAseq analysis
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to staff position within a Research Infrastructure? No Offer Description What we are mainly looking for (selection criteria can be found in the position description) • PhD in aquaculture, environmental
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techniques for the development of wearable devices capable of detecting plant health markers and analytes of agricultural interest. Candidates must hold a PhD degree in Chemistry, Materials Science, or related
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness