-
2D convolutional neural networks in Python. This is a part-time position (5 hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant
-
in AI and machine learning – from classical approaches to large language models. You are proficient in Python and key ML libraries (e.g. scikit-learn, PyTorch, LLM APIs), and you have a track record of
-
-scale job ads datasets, spatial datasets, patents). Conduct data analysis using econometric and statistical tools. Excellent knowledge of R is expected. Good knowledge of Python, experience with modern
-
/Python).Further information can be found at:https://www.findaphd.com/phds/project/?p185925If you are interested in this opportunity and would like to know more about it, please contact me via email at
-
: Essential criteria MSc. in Neuroscience, Physics, Computer Science, or a related field Strong background in computational neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and
-
, Skills and Experience Broad knowledge of statisticsSkills in programming languages such as R and/or Python Experience of applying statistics to solve complex real-world problems Ability to handle a diverse
-
of statistics and quantitative data analysis, hands-on experience with R or Python strong interest in prototyping commitment to and interest in the design and implementation of Open Science/Open Source practices
-
neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural
-
area, have extensive experience in conducting model-based economic evaluations using suitable statistical software (e.g. R or Python) and the ability to work independently, prioritise your own workload
-
area, have extensive experience in conducting model-based economic evaluations using suitable statistical software (e.g. R or Python) and the ability to work independently, prioritise own workload and