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Join a dynamic team of motivated individuals with deep collective experience throughout digital forensics, incident response, investigation, operations, and academic research. We seek individuals
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development, model hosting, deep learning, LLM fine-tuning experience (e.g., with huggingface transformers and parameter-efficient methods such as LoRA/QLoRA), model evaluation experience. (Please briefly
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) undergraduate teaching laboratory's curriculum. As deep-learning methods proliferate in neuroanatomy, this role sets the standard for scientific rigor - building models that respect spatial provenance, anatomical
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” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most
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research platform. We’ve stripped away the boundaries of what you can access and touch internally, so while you learn the intricacies of our industry, you’ll have plenty of opportunities to contribute and
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after one year of employment. Technical Skills or Knowledge: Proficiency in Python and deep learning frameworks such as Pytorch. Preferred Competencies Outstanding verbal and written skills
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electronic health record (EHR) data; apply ML methods (especially deep learning methods) to solve critical medical problems. Implement methods into software that meets research needs, manage and update source
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years
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etc; Candidates with multidisciplinary backgrounds are welcome. · Strong skills in computational and data analytical methodology development and implementation; experience in machine learning and deep