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As part of our Brain & AI team, you will be at the forefront of integrating artificial intelligence with brain-computer interface technologies. Our mission is to bridge the gap between AI and neuroimaging, driving innovation that is not only revolutionary but also accessible, transparent, and focused on privacy. We are seeking a motivated and skilled Machine Learning Engineer to develop AI models that enhance our understanding of neural mechanisms, applying this knowledge to real-world applications such as brain-computer interfaces.
You will play a pivotal role in pushing the boundaries of AI and neuroscience, tackling some of the most complex and fascinating challenges in the field today.
Responsibilities:
- Develop and evaluate scalable deep learning algorithms central to our brain decoding initiatives.
- Collaborate closely with data scientists on research in generative modeling and representation learning.
- Identify and solve bottlenecks in data processing pipelines, improving performance and reliability.
- Maintain high standards of code quality, organization, and automation across all projects.
- Adapt machine learning and neural network algorithms for optimized performance in various computing environments, including distributed clusters and GPUs.
- Write and revise papers, participate in conferences, and communicate results.
Basic Qualifications:
- Degree in Computer Science, Statistics, Informatics, Physics, Mathematics, Neuroscience, or another quantitative field.
- 3+ years of experience in the industry or research.
- Strong programming skills in Python, with experience in developing machine learning algorithms using Python and PyTorch.
- Experience in deep learning techniques such as supervised, semi-supervised, self-supervised learning, and/or generative modeling.
- Strong scientific background with the ability to formulate and test hypotheses through experiments.
- Proficient in managing unstructured datasets with strong analytical skills.
- Proven project management and organizational skills.
- Ability to support and collaborate with cross-functional teams in a dynamic environment.
Preferred Qualifications:
- PhD and research experience in a relevant quantitative field.
- Scientific publications in top-tier AI and neuroscience conferences or peer-reviewed journals.
- Familiarity with deep learning libraries such as PyTorch, Huggingface, and others.
- Hands-on experience with generative models and training frameworks.
- Experience with data and model visualization tools.
- Experience with non-invasive and invasive neural data.
Language Requirements:
- Excellent English communication skills are essential.
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