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Field
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Science, Engineering or other Machine Learning-related field. Programming experience in MATLAB, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in
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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
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checks that comply with IRB/ethical standards. Design and implement computer-vision algorithms. Develop, test, and refine deep-learning models (e.g., detection, segmentation, tracking) in PyTorch
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. Preferred: • Strong programming skills in languages such as R/Python • Research background in biostatistics/statistical genetics/population genetics/deep learning and LLM • Experience in any of the following
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-end technologies (e.g., Node.js, Python/Django/Flask, REST APIs, SQL/NoSQL databases) Demonstrated knowledge of AI/ML methods, such as supervised learning, NLP, or deep learning, with application
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. Li An, is seeking an undergraduate research assistant. The successful candidate will work with scientists and students to train deep learning models for detecting and identifying animal species in
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located in the heart of Detroit, Michigan where students from all backgrounds are offered a rich, high-quality education. Our deep-rooted commitment to excellence, collaboration, integrity, diversity and
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participate in the creation of next-generation drug discovery platforms for AMD. The successful applicant should have research experience in at least two of (a) deep learning and AI methods; (b) causal search
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organization skills. Experienced in workflow design and technical documentation. PREFERRED QUALIFICATIONS Experience developing AI methods for environmental data sets including working with deep learning
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imaging techniques (e.g. atomic force microscopy, confocal microscopy or electron microscopy) Evidence of experience in materials synthesis or characterisation e.g. rheology, deep eutectic solvents