Richmond, VA · On-site
$120 - $190/hr
... learning, and neural networks. Analyze large datasets, preprocess data, and extract meaningful ... Collaborate with software engineers to deploy and integrate AI models into production systems. Stay ...
Richmond, VA · On-site
$120 - $190/hr
... learning, and neural networks. Analyze large datasets, preprocess data, and extract meaningful ... Collaborate with software engineers to deploy and integrate AI models into production systems. Stay ...
A Neural Engineering Internship provides hands-on experience in the field of neural interfaces, brain-computer interfaces (BCIs), and neuromodulation. Interns typically work on signal processing, machine learning, and hardware development for neurotechnology applications. They may assist in designing experiments, analyzing neural data, or developing medical devices. This role is ideal for students in biomedical engineering, neuroscience, or computer science who want to gain practical experience in neurotechnology.
As a Neural Engineering Intern, you can expect to participate in a variety of hands-on projects such as analyzing neural data, helping design and test brain-computer interface systems, or developing algorithms for neural signal processing. You may also work closely with researchers and engineers to assist in laboratory experiments, device prototyping, or software development. Many internships include literature reviews, data visualization, and the opportunity to present findings to the team. These tasks are designed to give you a comprehensive introduction to current research and industry practices within neural engineering.
To thrive as a Neural Engineering Intern, you need a strong background in neuroscience, biomedical engineering, or electrical engineering, along with coursework or experience in signal processing and computational modeling. Familiarity with programming languages such as MATLAB or Python and use of lab equipment like EEG, MEG, or neural recording systems are highly beneficial. Critical thinking, effective communication, and a collaborative mindset help interns excel in research-driven, multidisciplinary teams. These skills enable interns to contribute meaningfully to projects, adapt to complex challenges, and succeed in this innovative field.
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$120 - $190/hr
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Posted 5 days ago
Jobs / AI Research Scientist - Machine Learning
AI Research Scientist - Machine LearningFull-time
About the RoleOur client is seeking a brilliant and innovative AI Research Scientist specializing in Machine Learning to join their cutting-edge R&D team in Richmond, Virginia, US. This role is at the forefront of developing next-generation AI technologies and algorithms. You will be responsible for designing, implementing, and evaluating advanced machine learning models, conducting groundbreaking research, andcontributing to high-impact AI applications. The ideal candidate possesses a strong academic background, a deep understanding of ML principles, and a passion for pushing the boundaries of artificial intelligence.Key Responsibilities:Conduct advanced research in machine learning, deep learning, and related AI fields. Design, develop, and implement novel algorithms and models for complex AI problems. Experiment with various ML techniques, including supervised, unsupervised, reinforcement learning, and neural networks. Analyze large datasets, preprocess data, and extract meaningful features for model training. Evaluate model performance, identify areas for improvement, and iterate on designs. Collaborate with software engineers to deploy and integrate AI models into production systems. Stay current with the latest advancements in AI and ML research through literature review and conference participation. Publish research findings in leading scientific journals and present at conferences. Mentor junior researchers and interns, fostering a collaborative research environment. Contribute to the intellectual property portfolio through patent applications.Qualifications:Ph.D. or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related quantitative field. Proven research experience demonstrated through publications in top-tier AI/ML conferences and journals (e.g., NeurIPS, ICML, ICLR, CVPR). Strong theoretical foundation in machine learning, deep learning, and statistical modeling. Proficiency in programming languages such as Python, and experience with ML libraries like TensorFlow, PyTorch, scikit-learn. Experience with data manipulation and analysis tools. Ability to design and conduct rigorous experiments, interpret results, and draw insightful conclusions. Excellent problem-solving skills and creativity in developing novel solutions. Strong communication and presentation skills, with the ability to articulate complex technical concepts. Experience with distributed computing frameworks (e.g., Spark) is a plus. Experience in specific domains like NLP, computer vision, or reinforcement learning is highly desirable. Join a forward-thinking team that is shaping the future of AI. This exciting opportunity is based in Richmond, Virginia, US .
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