AI Innovator for Stem Cell Differentiation
3 weeks ago
Join us at CellVoyant, where you'll be part of a dynamic team pushing the boundaries of biotechnology and AI. As a highly motivated and skilled Machine Learning scientist, your expertise in ML and computer vision will play a pivotal role in advancing our AI models, driving innovation, and transforming the field of stem cell differentiation.
About Our TeamWe're a biotech company passionate about harnessing AI to make a positive impact on society. With a strong foundation in research and a keen focus on innovation, we're constantly exploring new frontiers in biotechnology and AI.
Key Responsibilities- Research Innovation: Develop cutting-edge machine learning methodologies and contribute to the advancement of our AI models, enhancing their capabilities in predictive modelling, detection, classification, and pattern recognition of stem cell populations.
- Data Enhancement: Collect, process, and enrich multi-modal data sets (e.g., genomics and microscopy imaging) to enhance the robustness of our AI models, collaborating closely with computational biologists to ensure data relevance and quality.
- Data Collection Strategies: Design and implement effective data collection strategies to validate proof of concept for our AI models, focusing on microscopy data analysis and genomics-related hypotheses.
- Cross-Functional Collaboration: Collaborate with our interdisciplinary team, comprising computational biologists, biologists, data scientists, and software engineers, to drive innovative research projects and foster strong communication channels to integrate computer vision techniques into genomics research.
A Ph.D. degree in a technical subject (e.g., machine learning, AI, computer science, mathematics, physics, statistics). Excellent publication record in top-tier AI conferences such as NeurIPS, AAAI, ICLR, ICML, CVPR, ECCV, IJCV. Proficiency in programming languages like Python and cloud infrastructure (e.g., Google Cloud). Experience with relevant software (e.g., Github, Docker) and knowledge of ML/scientific libraries such as TensorFlow, PyTorch, NumPy, and Pandas.
Experience in Large-Scale System Design. Expertise in Self-Supervised ML and Generative Models such as Auto-encoders and GANs (Generative Adversarial Networks). Familiarity with Advanced Decision-Making Approaches such as Deep Reinforcement Learning (DRL), Imitation Learning (IL), and learning from demonstrations.
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