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fields • Prior experience in analysing EEG data. • Possess strong programming skills (mostly Python) and familiar with statistical methods. • Have good communication and writing skills
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motivated individual with capacity to work independently. Good time management and planning; Good publication track record; Some knowledge of Building Energy Modelling, GIS and programming language (Python, R
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postdoctoral experience). Proficient coding skills in at least one of Matlab, R and Python. Experience and proficient in processing different types of remote sensing datasets. Experience with terrestrial
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: Proficiency in Python and major deep learning frameworks such as PyTorch or TensorFlow Familiarity with transformer architectures and large language models (e.g., BERT, GPT) Experience in building, training
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simulations (LAMMPS, GROMACS). • Strong background in statistical mechanics and Python scripting (PyTorch/JAX for ML applications). • Ability to independently design and execute research projects, with a track
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planning. b) Have knowledge of statistical computing, data science, data modelling, or data visualisation languages and software (e.g., PowerBI, Python, R, SAS, SPSS, SQL, Tableau, etc.) c) Have experience
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, and machine learning. Expert skills with at least one data analysis environment (e.g., R, Stata or SAS). Skills in machine learning and programming proficiency in languages like JavaScript and Python
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or adaptive designs. • Proficiency in at least one of the following: R, Python, JavaScript, or other relevant data analysis or app development tools. • Familiarity with EMA, EMI, MRTs, or SMART designs is
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3 years postdoctoral experience). Proficient coding skills in at least one of Matlab, R and Python. Experience working with remote sensing. Experience with crop physiology is a plus. Strong numerical
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in Python, R, or similar programming languages; • Experience with network analysis, process mining, and time-series data analysis is a plus • Strong analytical, problem-solving, and communication