462 machine-learning-"https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" positions in Belgium
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in daily research. The successful candidate: has knowledge in general wet lab practice (e.g. tissue processing, DNA/RNA processing, molecular biology) and willingness to learn new techniques and
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in daily research. The successful candidate: has knowledge in general wet lab practice (e.g. tissue processing, DNA/RNA processing, molecular biology) and willingness to learn new techniques and
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of statistics, computer science and/or machine learning Interest in biology or molecular biology, microbial ecology Proficiency in programming languages such as Python, R and/or C++ as well as Linux systems
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» OtherEducation LevelPhD or equivalent Skills/Qualifications Who you are PhD in Computer Science, Machine Learning or related field or 5+ years of relevant industrial experience. Strong track record of developing
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machine learning. This encompasses application domains such as industrial inspection, (ultra) high-definition video enhancement, smart multi-camera networks, computer vision, sensor fusion, and (medical
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of thermal energy and (hybrid) machine learning in which physics-based models are combined with data-driven techniques. Thermal cycles make it possible to meet the demand for heating, cooling, and electricity
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students working on the early modern and modern Arabic corpus within the project You will be expected to design and teach academic courses on Arabic epigraphy and fieldwork methods, You will be responsible
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. For more information about this vacancy, please contact prof. Petra Van Damme (petra.vandamme@UGent.be , +32 (0)9/264 51 29). Where to apply Website https://academicpositions.com/ad/ghent-university/2026
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-impact weather A dedicated computer scientist/software engineer works alongside you to set up ICON-CLM on the HPC systems, support simulations and manage large data volumes—so you can focus on the science
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leverage state of the art machine learning models (AlphaFold2, RFdiffusion) and multi-omics data integration to guide the rational design and optimization of therapeutic antibodies. Overall, you will have