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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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, particularly deep learning and optimization methods Excellent coding skills, particularly in Python and machine learning frameworks (PyTorch or Jax) The ability for creative and analytical thinking across
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Description We offer a deep immersion in bio-based energy technologies; the candidate will learn and live the translational perspective of designing biomaterials for sustainable energy-related applications
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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(TV-L Brandenburg). Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use dynamics and the economic trade-offs involved. We aim to develop and
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: Building interpretable causal models to explain patterns (e.g., congestion dynamics), enabling transparency in high-stakes decision-making. We combine statistical data mining, deep learning, and domain
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-driven to take a deep dive into the unknown. You’re extremely capable, using creativity and ingenuity to rise to new challenges. You’ve got an excellent M.Sc. degree in cancer genetics, molecular biology
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within