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Field
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analysing of point cloud data mainly from airborne LiDAR (ALS) but potentially also from terrestrial and mobile sources (TLS & MLS). The goal of the project is to uncover the efficacy of using airborne LiDAR
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– ‘“Seeing through clouds” satellite remote sensing by unifying optical and SAR sensors’. Qualifications Applicants should have a doctoral degree or an equivalent qualification and must have no more than five
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regular meetings with the research group. Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or a related field. Proven experience working with LiDAR sensors, point cloud data
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Engineering, Computer Science, or a related field. Proven experience working with LiDAR sensors, point cloud data processing, and sensor integration. Strong background in signal processing, computer vision, or
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tools (e.g. kneadata, metaphlan2, HUMAnN) Experience working in high performance computing cluster Experience in AI/ML, cloud computing, web development are assets Hiring Institution: LKC
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. Proficiency in R and Python; comfortable on Linux clusters. Bonus points for Julia or cloud‑native workflows. Proven analytical reasoning—able to translate numbers into biological insight. Desire to innovate
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operationalise of remote sensing processing chains and their deployment in cloud using the latest approaches to the containisation and orchestration Grade 8 Lead the development of new and innovative optical
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cancer genomics and functional interpretation of genetic variants Proficiency in Python, R, or other bioinformatics languages Knowledge of cloud computing, and high-performance computing (HPC) environments
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Aerosol Injection (SAI), which creates a protective aerosol layer; 2) Marine Cloud Brightening (MCB), which increases cloud reflectivity over oceans. While these methods could slowdown warming
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attacks, SQL injection, and cheating. Experience in multiplayer game services such as Azure PlayFab, AWS GameLift, Google Cloud Servers, and other similar services. Strong knowledge in C++ will be