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, or similar. Good knowledge in programming (e.g. C or python) Experience in 3D image segmentation. Track record of producing high-quality research Documented pedagogical skills and pedagogical training
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or Python Machine learning methods (for the baseline prediction for the reward funds) is beneficial We expect: Strong motivation to contribute to policy-relevant research Strong interest in teamwork and
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. Required Qualifications: A doctoral degree (PhD, MD, or equivalent) conferred by the start date. Proficiency in R/Python Experience with scRNAseq, and/or spatial proteomic/transcriptomic data analysis Growth
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Programming skills in Python Experience with Machine learning (PyTorch/Keras). A computational mindset and a strong interest in computational biology We offer A versatile and challenging job with
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the start of the appointment, Experience developing complex models for real world data, with strong programming skills (R, Python, and/or Julia), and Experience interacting with and communicating complex
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skills in Python Demonstrated data analysis and visualization expertise Experience working in high-performance computing environments Familiarity with data analysis and visualization tools Preferred
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: Ph.D. in Computational Biology, Bioinformatics, Computer Science or related field. Demonstrated proficiency in Python or R, with experience building bioinformatic workflows and documenting code with
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of ecological, coastal, and geological research as well as perform analyses with Remote Sensing (optical and lidar), Geographic Information Systems (GIS), Python, R, and/or other programming languages or image
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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(e.g., Matlab, Python), neuroinformatics, and/or advanced microscopy tools is plus, but not required. More details of the lab including publications can be found in the lab web site (https://kimlab.io