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Field
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machine learning methods • Proficiency in transport planning software such as EMME or PTV Visum. • Programming expertise in at least one language, e.g., Python, Java, or C++. • Strong written and
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optimization algorithms is highly desirable Excellent programming skills in Python, C++, Java, Julia, or other relevant languages Knowledge of maritime decarbonization and alternative fuels is a plus A good
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++, Java, Julia, or other competent languages. A good record of publications in reputable peer-reviewed journals in maritime transport, logistics management, machine learning, deep learning, and optimization
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experience may include tools and languages such as Python, MATLAB, R, Java, and optimisation packages or solvers such as Gurobi, CPLEX, Pyomo or equivalent platforms. Experience with modelling or simulation
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such as Python, MATLAB, R, Java, and optimisation packages or solvers such as Gurobi, CPLEX, Pyomo or equivalent platforms. Experience with modelling or simulation environments such as AnyLogic, MATSim
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university; Excellent programming skills, such as Python, C++, Java, Julia, or other competent languages; A good record of publications in reputable peer-reviewed journals or conferences in maritime transport
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Models. Experience with deep learning frameworks such as PyTorch or TensorFlow. Proficiency in programming languages including C/C++, Python, Java, and Go. Familiarity with Digital Content Forensics
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such as Python, C/C++, and Java. Experience with large language models (LLMs) applied to security tasks, such as threat detection or attack analysis. We regret to inform that only shortlisted candidates
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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, the final version of the thesis and a complete transcript of grades on the master’s degree must be provided at the time of the interview. a solid grasp on object-oriented programming (eg. C++, Java, Kotlin