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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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combine it with extensive simulation studies. As currently defined, it will rely on three main methodological domains: (1) active learning, (2) causal machine learning and (3) policy evaluation. It is an
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning
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you will: Develop a theoretical framework for designing accessible and inclusive infrastructure and wayfinding systems. Develop VR simulators for people with visual, hearing, motor, and cognitive
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. Options model for additional leave, pension, a bicycle or personal training. Moving allowance (subject to conditions). 30% rule for international employees (if applicable). Pension through ABP. Ample
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employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in your personal and professional development. For more information, please visit Working
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support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In the FlexMobility project we propose a holistic
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, machine learning and behavioral modeling methodologies. In the FlexMobility project we propose a holistic approach to design a public transport network that includes both traditional fixed lines and
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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vivo evaluations using soft-tissue models to simulate gastrointestinal navigation and refine the system’s interface and functionality based on clinician feedback. Contribute to preclinical deployment