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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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of optimization and machine learning. • Knowledge of reinforcement learning and black box optimization would be a plus. Skills • The candidate must be comfortable with algorithmic development using
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for future career development in both academia and tech. Profile Required: PhD in ML, computational neuroscience, physics, engineering, or related field Strong experience in machine learning (PyTorch
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, France [map ] Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/13 04:59 AM UnitedKingdomTime** (posted 2025/10/21 05:00 AM UnitedKingdomTime, listed until 2026/04/22 04:59 AM
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for embedded and GPU platforms. Collaborate with ARSPECTRA engineers and surgeons to create a complete AR guidance pipeline : tracking, SLAM, gaze, user interface Your profile PhD in machine learning
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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Your Privacy When you visit any website, it may store or retrieve information on your browser, mostly in the form of cookies. This information might be about you, your preferences or your device and is mostly used to make the site work as you expect it to. The information does not usually...
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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision