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Field
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, Computational Biology, Bioinformatics, or a closely related discipline. Demonstrated experience programming in Python or other major programming languages. Proven experience working in Linux environments
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and statistical skills, with proficiency in R or Python Experience working with longitudinal data, infectious disease epidemiology, or high-dimensional datasets Demonstrated track record of peer
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, computational modeling, computer vision, natural language processing, or related AI-based methods. Strong programming skills, preferably in Python. Experience working with complex, multimodal datasets and
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AI and machine learning methods applied to cancer genomics. Proficiency in at least one modern programming language. Experience with a scientific programming environment, such as python or R, is
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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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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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analysis skills (e.g., Python, MATLAB, LabVIEW). PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application”. If you do not already have educational
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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
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experiment codebases (prefer Python). Apply causal inference and discovery frameworks to clinical questions. Translate proposed methods and frameworks into real-world clinical workflows. Contribute to grant