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worked in or with the defence sector Has programming experience in Python and Pyomo Due to the position’s close collaboration with the Swiss defence industry and the armed forces, preference will be given
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Programming skills (commonly C++, Python, or similar languages used in HEP frameworks), Monte Carlo simulation methods and data analysis techniques Interest in connecting ideas and people across disciplines
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for emulating constrained optimization problems Advanced programming skills in Python, with deep experience in libraries such as PyTorch, Pyomo, and the broader scientific stack (Xarray, Pandas). Knowledge
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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or Master's level) Interest in geophysics, civil engineering, or data-driven infrastructure monitoring Practical, hands-on attitude for field and laboratory work Programming experience (Python preferred
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related field with experience in water chemistry, silviculture, experimental field work and large-scale data analysis. You must have good statistical skills and programming experience (e.g., in R or Python
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phosphorus-driven eutrophication, with experience in soil monitoring and erosion processes. Skills in GIS, spatial analysis of large scale data, modelling, remote sensing, and programming (R or Python
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, ASAP, or equivalent) Programming skills in Python, MATLAB, or similar Ability to work independently as well as in interdisciplinary teams Very good command of English (written and spoken) Preferred
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mechatronics systems is a plus Excellent coding skills in python, ROS, and RL&IL pipeline experience on simulator and training libraries. Knowledge on C++ is a plus Experience with Physics simulators such as
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Strong programming skills in Python (e.g., PyTorch) Experience with data processing, visualization, and experimentation workflows Knowledge of additive manufacturing processes or industrial monitoring