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Field
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robots, robotic manipulators, or locomotion systems. Knowledge of machine learning, reinforcement learning, imitation learning, or computer vision techniques for robotics applications. Strong analytical
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with the Ganna Lab (https://www.dsgelab.org/ ) at FIMM and the Probabilistic Machine Learning Lab (https://www.helsinki.fi/en/researchgroups/probabilistic-machine-learning ; groups of Acerbi and Klami
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learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP, BLIP) or scene-graph inference is a plus. Key Competencies Strong software development and debugging skills. Able
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and experience of protein molecular modelling, protein–ligand docking, machine learning methods for computational biology, and software development in Python. Please upload your CV (including
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the possibility of extension. For more details on our research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu For questions, please email mweber
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pathophysiological mechanisms for acute organ injury research under POI-KB. · Authorship of high-impact first-author or co-author manuscripts in leading informatics and machine learning journals and conferences
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and the Probabilistic Machine Learning Lab (https://www.helsinki.fi/en/researchgroups/probabilistic-machine-learning ; groups of Acerbi and Klami) at the Department of Computer Science, aligning with
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to regulatory agencies, investors, pharma companies etc. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/human-organs-on-chips/ What you’ll do: Develop models
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and the Probabilistic Machine Learning Lab (https://www.helsinki.fi/en/researchgroups/probabilistic-machine-learning ; groups of Acerbi and Klami) at the Department of Computer Science, aligning with
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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno