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spectrometry, and untargeted data science workflows. Proficiency in chemometric methods and/or python programming will be an advantage. Candidates are expected to be enthusiastic and adaptable to working in
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, electrical engineering, communication engineering, computer science, or a related field. Documented experience with deep learning techniques (e.g., CNNs, Transformers) Strong programming skills in Python and
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of the following qualifications: Extensive experience in programming using Python, R, or other languages Research experience in greenhouse gases, or ecological experiments Insight into global
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possible. It is strictly required that you have experience with: Scientific programming, preferably in python and/or MATLAB and/or C++ Derivation and implementation of finite element methods (FEM) in code
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(deep neural networks) Probability theory Computer vision Robotics Programming skills (Python, C++) and ML libraries (PyTorch, Tensorflow) Preferably, the candidate has experience with: Bayesian machine
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Experience with VLSI design (Cadence tools, Verilog/VHDL, SPICE) Knowledge of neural networks and neuromorphic systems is a strong advantage Good programming skills (e.g., Python, MATLAB) and interest in
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, preferably Reinforcement Learning (e.g., Q-learning, Deep Q-Networks) or other control algorithms. Proficiency in Python, MATLAB, or similar for data analysis, modeling, or AI implementation. Strong written
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-years of work experience) related to data science, bioinformatics, computer science or within natural sciences Excellent programming skills, preferably in Python and/or R Good software engineering
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and fabrication. The ideal candidates have extensive experience with: Programming of IO boards (STM32, Pixhawk, BeagleBone, etc.) in different programming languages (C++, Python, etc.), MATLAB/Simulink
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, urban or trade models. Proficiency in Stata. Knowledge of R, Matlab and/or Python. Prior experience in working with spatial data. Documented research track record at international level, including