23 programming-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Luxembourg
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national projects funded by the National Research Fund (FNR) and local industries, and international research projects funded by the EU FP7 program, H2020 program and the European Space Agency (ESA
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cohesive learning experience for students. Research Portfolio The candidate is expected to have an internationally recognised research profile and to bring a coherent research programme in public health
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. program that includes training in transferable skills, support for career development, lectures and teaching by international experts and annual Ph.D. symposia. Upon successful completion of the doctoral
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of analysis pipelines Development, maintenance and application of programming scripts for omics data analysis and creation of network and machine learning models Data integration support for analysis pipelines
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Mathematics, or a closely related field A strong interest in machine learning, AI, security, reliability, and the latest technologies Strong programming skills in Python or other programming languages Interest
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profile Master in Computer Science. Major in machine learning or computer vision is an asset Strong programming skills Strong analytical skills Industry experience in information and communication
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platforms (e.g., USRPs), RF hardware, or RFSoC platforms Programming experience in C/C++, Python, MATLAB, or FPGA development Experience with laboratory measurements and system integration Preferred
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Lists of publications, research projects, PhD theses supervised and subjects taught if applicable Research statement and research plan for the next five years, including proposed research projects in
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propagation, pointing, acquisition and tracking, and atmospheric effects Programming and instrument-control skills, e.g. Python, MATLAB, LabVIEW, for data acquisition and analysis A genuine interest in pursuing
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intelligence and large language models for data analysis, and bioscientific programming. The candidate should have experience in the analysis of large-scale biomedical data, e.g.: omics or clinical data, using