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optimisation Experience in one or more of the following areas would be advantageous: EO or geospatial data analysis, parallel or distributed computing, GPU programming, Linux and containerised
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and sufficient. Assigning meaning to prototypes is however a time-consuming task, as the various models tend to learn prototypes that do not carry sufficiently high-level meaning. A parallel line of
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, parallel research axes: pushing model accuracy on systems of several thousand atoms with an IWAE architecture; equipping it with the ability to quantify its own uncertainty — a prerequisite for any use
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computing Excellent programming skills (e.g., Python, C/C++, Julia, or MATLAB) Interest in quantum computing, quantum algorithms, and hybrid quantum–classical computing. Previous experience is considered
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neuroscience or a related field of study Extensive experience with programming (Python or Matlab) Passionate about human neuroanatomy and cognitive neuroscience Highly advanced and flexible command of
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statistical physics; expertise in quantum information is particularly appreciated Strong programming experience and/or strong background in analytical methods Very good command of written and spoken
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Current master's student in Process Systems Engineering, Computational Engineering Science, Chemical Engineering or a comparable program Programming experience in Python; experience with PyTorch is a
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design and training of neural networks Practical experience in FPGA development or embedded hardware design, including HDL (VHDL/Verilog) or high-level synthesis (HLS) tools Proficiency in programming
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. Your Profile Currently pursuing a master’s degree in Computer Science, Data Science, Artificial Intelligence, Geosciences, Physics, or a related field Strong programming skills in Python and experience
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, or a related field Proficiency in at least one programming language (Python, C++, …) Experience in neuroscience is an advantage Good analytical skills with a sound understanding of data evaluation