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chemistry preferred) researchers. Track Record: A strong publication record in NLP, ML, Multimodality, Information Retrieval, or Chemistry. Technical Skills: Proficiency in Python and PyTorch/JAX is required
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algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly for developing Windows desktop application software incorporating deep learning models Hold at least a
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of large-scale genomic and transcriptomic datasets Experience in high-performance computing environments (e.g., command-line interface, scripting in R/Python, use of common bioinformatics software). Ability
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Language Models Proficiency in Python and deep learning frameworks (PyTorch/TensorFlow) On top the candidate with two or more of the following will be an added advantage: Ability to conduct independent
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data architectures and tools for automating data analysis. Relevant software platforms include Matlab and Python. Develop physics-based computational models (e.g., finite element analysis) to complement
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developing and teaching spatial methods, especially desktop GIS, R, and Python strongly preferred. Desirable Qualifications: Interest/experience in spatial AI approaches. Comfort with research project
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and identify green skill gaps within the prison context. Phase 2: Data Modelsling and Structuring: Utilize data analysis techniques (e.g., Python and statistical modelling) to process information
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. We will also consider candidates who have extensive C++ or Python coding knowledge as these are transferrable to R. The candidate will be working with the Principal Investigator(s) on the analysis
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animal models. Strong technical skills in setting up and troubleshooting complex experimental systems. Proficiency in programming and data analysis (e.g., MATLAB, Python). Demonstrated ability
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with programming in Python is a requirement Experience with telecentric particle imagers, image analysis, and machine learning for particle recognition is an advantage Experience of working with wave